{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 3: Black Litterman Mean Risk Optimization\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  25 of 25 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-2.0257%</td>\n",
       "      <td>0.4057%</td>\n",
       "      <td>0.4035%</td>\n",
       "      <td>1.9693%</td>\n",
       "      <td>0.0180%</td>\n",
       "      <td>0.9305%</td>\n",
       "      <td>0.3678%</td>\n",
       "      <td>0.5783%</td>\n",
       "      <td>0.9483%</td>\n",
       "      <td>-1.1954%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5881%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>2.8236%</td>\n",
       "      <td>0.9758%</td>\n",
       "      <td>0.6987%</td>\n",
       "      <td>1.7539%</td>\n",
       "      <td>-0.1730%</td>\n",
       "      <td>0.2410%</td>\n",
       "      <td>1.3734%</td>\n",
       "      <td>-1.0857%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-11.4863%</td>\n",
       "      <td>-1.5879%</td>\n",
       "      <td>0.2412%</td>\n",
       "      <td>-1.7557%</td>\n",
       "      <td>-0.7727%</td>\n",
       "      <td>-1.2473%</td>\n",
       "      <td>-0.1736%</td>\n",
       "      <td>-1.1239%</td>\n",
       "      <td>-3.5867%</td>\n",
       "      <td>-0.9551%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5547%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>0.1592%</td>\n",
       "      <td>-1.5646%</td>\n",
       "      <td>-0.1466%</td>\n",
       "      <td>-1.0155%</td>\n",
       "      <td>-0.7653%</td>\n",
       "      <td>-3.0048%</td>\n",
       "      <td>-0.9034%</td>\n",
       "      <td>-2.9145%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-5.1389%</td>\n",
       "      <td>-4.1922%</td>\n",
       "      <td>-1.6573%</td>\n",
       "      <td>-2.7699%</td>\n",
       "      <td>-1.1047%</td>\n",
       "      <td>-1.9769%</td>\n",
       "      <td>-1.2207%</td>\n",
       "      <td>-0.8855%</td>\n",
       "      <td>-4.6059%</td>\n",
       "      <td>-2.5394%</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2066%</td>\n",
       "      <td>-3.0309%</td>\n",
       "      <td>-1.0410%</td>\n",
       "      <td>-3.1557%</td>\n",
       "      <td>-1.6148%</td>\n",
       "      <td>-0.2700%</td>\n",
       "      <td>-2.2845%</td>\n",
       "      <td>-2.0570%</td>\n",
       "      <td>-0.5492%</td>\n",
       "      <td>-3.0019%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.2737%</td>\n",
       "      <td>-2.2705%</td>\n",
       "      <td>-1.6037%</td>\n",
       "      <td>-2.5425%</td>\n",
       "      <td>0.1099%</td>\n",
       "      <td>-0.2241%</td>\n",
       "      <td>0.5707%</td>\n",
       "      <td>-1.6402%</td>\n",
       "      <td>-1.7641%</td>\n",
       "      <td>-0.1649%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1538%</td>\n",
       "      <td>-1.1366%</td>\n",
       "      <td>-0.7308%</td>\n",
       "      <td>-0.1448%</td>\n",
       "      <td>0.0895%</td>\n",
       "      <td>-3.3839%</td>\n",
       "      <td>-0.1117%</td>\n",
       "      <td>-1.1386%</td>\n",
       "      <td>-0.9719%</td>\n",
       "      <td>-1.1254%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-4.3384%</td>\n",
       "      <td>0.1693%</td>\n",
       "      <td>-1.6851%</td>\n",
       "      <td>-1.0215%</td>\n",
       "      <td>0.0915%</td>\n",
       "      <td>-1.1791%</td>\n",
       "      <td>0.5674%</td>\n",
       "      <td>0.5287%</td>\n",
       "      <td>0.6616%</td>\n",
       "      <td>0.0330%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6435%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9869%</td>\n",
       "      <td>-0.1450%</td>\n",
       "      <td>1.2224%</td>\n",
       "      <td>1.4570%</td>\n",
       "      <td>0.5367%</td>\n",
       "      <td>-0.4607%</td>\n",
       "      <td>0.5800%</td>\n",
       "      <td>-1.9918%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 APA       BA      BAX      BMY    CMCSA      CNP      CPB  \\\n",
       "Date                                                                         \n",
       "2016-01-05  -2.0257%  0.4057%  0.4035%  1.9693%  0.0180%  0.9305%  0.3678%   \n",
       "2016-01-06 -11.4863% -1.5879%  0.2412% -1.7557% -0.7727% -1.2473% -0.1736%   \n",
       "2016-01-07  -5.1389% -4.1922% -1.6573% -2.7699% -1.1047% -1.9769% -1.2207%   \n",
       "2016-01-08   0.2737% -2.2705% -1.6037% -2.5425%  0.1099% -0.2241%  0.5707%   \n",
       "2016-01-11  -4.3384%  0.1693% -1.6851% -1.0215%  0.0915% -1.1791%  0.5674%   \n",
       "\n",
       "                 DE      HPQ      JCI  ...       NI     PCAR      PSA  \\\n",
       "Date                                   ...                              \n",
       "2016-01-05  0.5783%  0.9483% -1.1954%  ...  1.5881%  0.0212%  2.8236%   \n",
       "2016-01-06 -1.1239% -3.5867% -0.9551%  ...  0.5547%  0.0212%  0.1592%   \n",
       "2016-01-07 -0.8855% -4.6059% -2.5394%  ... -2.2066% -3.0309% -1.0410%   \n",
       "2016-01-08 -1.6402% -1.7641% -0.1649%  ... -0.1538% -1.1366% -0.7308%   \n",
       "2016-01-11  0.5287%  0.6616%  0.0330%  ...  1.6435%  0.0000%  0.9869%   \n",
       "\n",
       "                SEE        T      TGT      TMO      TXT       VZ     ZION  \n",
       "Date                                                                       \n",
       "2016-01-05  0.9758%  0.6987%  1.7539% -0.1730%  0.2410%  1.3734% -1.0857%  \n",
       "2016-01-06 -1.5646% -0.1466% -1.0155% -0.7653% -3.0048% -0.9034% -2.9145%  \n",
       "2016-01-07 -3.1557% -1.6148% -0.2700% -2.2845% -2.0570% -0.5492% -3.0019%  \n",
       "2016-01-08 -0.1448%  0.0895% -3.3839% -0.1117% -1.1386% -0.9719% -1.1254%  \n",
       "2016-01-11 -0.1450%  1.2224%  1.4570%  0.5367% -0.4607%  0.5800% -1.9918%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Black Litterman Portfolios\n",
    "\n",
    "### 2.1 Calculating a reference portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1590%</td>\n",
       "      <td>11.5019%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.4807%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8193%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.8262%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.1805%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.2738%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.0000% 6.1590% 11.5019% 0.0000% 0.0000% 8.4807% 0.0000% 3.8193%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.8262% 0.0000% 0.0000% 0.0000% 0.0000% 7.1805%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.0000% 0.0000% 4.2738% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "port.alpha = 0.05\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w, title='Sharpe Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disabled</th>\n",
       "      <th>Type</th>\n",
       "      <th>Set</th>\n",
       "      <th>Position</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type Relative</th>\n",
       "      <th>Relative Set</th>\n",
       "      <th>Relative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Energy</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>8.0000%</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Financials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>10.0000%</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Materials</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>9.0000%</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Industry</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disabled     Type       Set          Position Sign   Weight Type Relative  \\\n",
       "0     False  Classes  Industry            Energy   >=  8.0000%       Classes   \n",
       "1     False  Classes  Industry  Consumer Staples   >= 10.0000%       Classes   \n",
       "2     False  Classes  Industry         Materials   >=  9.0000%       Classes   \n",
       "\n",
       "  Relative Set     Relative  \n",
       "0     Industry   Financials  \n",
       "1     Industry    Utilities  \n",
       "2     Industry  Industrials  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.ConstraintsFunctions as cf\n",
    "\n",
    "asset_classes = {'Assets': ['JCI','TGT','CMCSA','CPB','MO','APA','MMC','JPM',\n",
    "                            'ZION','PSA','BAX','BMY','LUV','PCAR','TXT','TMO',\n",
    "                            'DE','MSFT','HPQ','SEE','VZ','CNP','NI','T','BA'], \n",
    "                 'Industry': ['Consumer Discretionary','Consumer Discretionary',\n",
    "                              'Consumer Discretionary', 'Consumer Staples',\n",
    "                              'Consumer Staples','Energy','Financials',\n",
    "                              'Financials','Financials','Financials',\n",
    "                              'Health Care','Health Care','Industrials','Industrials',\n",
    "                              'Industrials','Health care','Industrials',\n",
    "                              'Information Technology','Information Technology',\n",
    "                              'Materials','Telecommunications Services','Utilities',\n",
    "                              'Utilities','Telecommunications Services','Financials']}\n",
    "\n",
    "asset_classes = pd.DataFrame(asset_classes)\n",
    "asset_classes = asset_classes.sort_values(by=['Assets'])\n",
    "\n",
    "views = {'Disabled': [False, False, False],\n",
    "         'Type': ['Classes', 'Classes', 'Classes'],\n",
    "         'Set': ['Industry', 'Industry', 'Industry'],\n",
    "         'Position': ['Energy', 'Consumer Staples', 'Materials'],\n",
    "         'Sign': ['>=', '>=', '>='],\n",
    "         'Weight': [0.08, 0.1, 0.09], # Anual terms \n",
    "         'Type Relative': ['Classes', 'Classes', 'Classes'],\n",
    "         'Relative Set': ['Industry', 'Industry', 'Industry'],\n",
    "         'Relative': ['Financials', 'Utilities', 'Industrials']}\n",
    "\n",
    "views = pd.DataFrame(views)\n",
    "\n",
    "display(views)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-50.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-25.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-20.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2\n",
       "0  100.0000%   0.0000%   0.0000%\n",
       "1  -20.0000%   0.0000%   0.0000%\n",
       "2    0.0000%   0.0000%   0.0000%\n",
       "3    0.0000%   0.0000%   0.0000%\n",
       "4    0.0000%   0.0000%   0.0000%\n",
       "5    0.0000% -50.0000%   0.0000%\n",
       "6    0.0000%  50.0000%   0.0000%\n",
       "7    0.0000%   0.0000% -25.0000%\n",
       "8    0.0000%   0.0000%   0.0000%\n",
       "9    0.0000%   0.0000%   0.0000%\n",
       "10 -20.0000%   0.0000%   0.0000%\n",
       "11   0.0000%   0.0000% -25.0000%\n",
       "12 -20.0000%   0.0000%   0.0000%\n",
       "13   0.0000%  50.0000%   0.0000%\n",
       "14   0.0000%   0.0000%   0.0000%\n",
       "15   0.0000% -50.0000%   0.0000%\n",
       "16   0.0000%   0.0000% -25.0000%\n",
       "17 -20.0000%   0.0000%   0.0000%\n",
       "18   0.0000%   0.0000% 100.0000%\n",
       "19   0.0000%   0.0000%   0.0000%\n",
       "20   0.0000%   0.0000%   0.0000%\n",
       "21   0.0000%   0.0000%   0.0000%\n",
       "22   0.0000%   0.0000% -25.0000%\n",
       "23   0.0000%   0.0000%   0.0000%\n",
       "24 -20.0000%   0.0000%   0.0000%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0  8.0000%\n",
       "1 10.0000%\n",
       "2  9.0000%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P, Q = cf.assets_views(views, asset_classes)\n",
    "\n",
    "display(pd.DataFrame(P.T))\n",
    "display(pd.DataFrame(Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.5336%</td>\n",
       "      <td>5.0288%</td>\n",
       "      <td>11.1747%</td>\n",
       "      <td>0.0108%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.3380%</td>\n",
       "      <td>5.8493%</td>\n",
       "      <td>0.6307%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8559%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.1654%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.8844%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.5124%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.5336% 5.0288% 11.1747% 0.0108% 0.0000% 2.3380% 5.8493% 0.6307%   \n",
       "\n",
       "            HPQ     JCI  ...      NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 4.8559% 0.0000% 0.0000% 8.1654% 0.0000% 6.8844%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.0001% 0.0000% 4.5124% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate Black Litterman inputs:\n",
    "\n",
    "port.blacklitterman_stats(P, Q/252, rf=rf, w=w, delta=None, eq=True)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='BL'# Black Litterman\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = False # Use historical scenarios for risk measures that depend on scenarios\n",
    "\n",
    "w_bl = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w_bl.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TbSePHrCfOpaoVxS3TaszbM3LubBKCOU/AFuFELlqZ2aMMZaPC2TGmEdcbJOoB+A2s2/AnU6Xo5Xidpur1Yy21m/UzFgnJl5Xq14cqteKhlanVzuuaoQQOJuahMP7duDg7s2Ovds3WJOTjpoMBmOyIsR6a17OGgCbABwQQvA8zYwxpgIukBljJUZENQB08TH79XW5XF10Or0+vlUnNGnVxdQgvhWqRkRBkng2yRtxOh04cXgfDu/bjn07NuYd3L1F5GRnagxG0568nOyFQii/A9jLbRmMMVY2uEBmjN00IgoD0MXoY74DAt0A+Ddq1t7VtG1Xc6Nm7RFWrabX9QmXVzkXMnBw91Zs27DCtumfP902S55dkqTfrJbcRQBWCSGy1c7IGGMVFRfIjLHrIqJYjUY7UGcwDnW7nBEN4ls5mrXr5hvXtB2iasdwQVwGhBBISTqK7RtXiw2rl+YcPbjLYDCa9ublZP8khPIbgH08uswYY57DBTJj7DIXe4njNFrdQK1OP0wja6p06HG33K7bXfrouOaQZVntiJWezZqHvdv+xZb1f9k3//Ony2a1FIwuzwawmnuXGWPs1nCBzBgrKIrjtTr9II1GO1Sr0/t3vL2/tl23frp6sU24j9iLCSGQfPIItm1YKVYtnZN7Lu20G8APdpv1WwA7eWSZMcaKjwtkxioxImqi1ekHazTaB3QGo2+n2+/Rtut2l65ugwRunSinkk8ewdo/f3atXPKj3WG3Zdjt1i/dLtcPQogTamdjjLHyggtkxioZIjIDGGQy+72g0Wgjb7tjkK5d97u0taMbcVFcgQghkLhnK1b/Ns+2fsUiyBptYl7OhRkAFgghMtXOxxhj3owLZMYqCSJqbDD6jHK7XYMbNmnj7jPwUXN8y87cU1wJOJ0O7Pjvb6xc8mPers1rNTq94Z+8nAsfAPhLCKGonY8xxrwNF8iMVWAXl3W+z8fs9zxJUu1e9wzXde83VBNcNULtaEwlebnZ2LB6KRbP/iQ3K+NcltWa9waEmC2EyFM7G2OMeQsukBmrgIgoRm8wPSMU5cF6DZsqfQc95tusbTfIGo3a0ZiXEEJg/46N+GX29Nx92zcSiL502K3ThBBJamdjjDG1cYHMWAVxcSaKLiaz35sQovHtdz+o6XH3UG1oRA21ozEvl5Z8Esvmf2lftXSOkDXa1Zbc7DcAbOQZMBhjlRUXyIyVcxcL414mH9+3jD7m2oMfe8mnfY+7odXq1I7GyhlLXg5WL5snFv3wicVus5625GZPRv5FfQ61szHGWFniApmxcoqIJAD9jD6+b/kHVAkf/MRL5jZd+vJFd+yWud1ubN+wCgtnfZR78sh+p9PpeE1xu78QQtjVzsYYY2WBC2TGyhki0gAYaDSZ36wSGhH0wJPjzc3bdefFPFipOJq4G7OmT8o7tG+71WG3viyEmCmEcKqdizHGShMXyIyVE0SkI6KheoNpSkRUHfMDT/7PHN+yE89dzMrEwd1b8P3Hr+UmHTuYY7PmvQBgHi9pzRirqLhAZszLEZFMRA/q9Ma3a0fHGQY/8bK5YZM2asdildSerevx7UcTc8+mJJ23WnLHAFjEF/MxxioaLpAZ82JE1MNoMn8WXr121UfHvmmu36i52pEYgxAC2zasxPcfv5abmX4m2WrJfQ7An1woM8YqCi6QGfNCRNTI5OM7w2DyaTJizJs+rTr14lYK5nUURcGmtb/j+49fy8vNuXDEmpczUgjxr9q5GGPsVnGBzJgXIaJgsxbfKyTffv/I16Uedw+TeLo25u3cbjfW/fULvvtoosXldP5hteSOEkKkqJ2LMcZKii97Z8wLEJFGK9MzRg2O399I252EWxMRVZuLY1YuyLKMzr3uxee/bDXd3v/Bvjq94bBWp3+ZiPgHmDFWLvEIMmMqI6JOvjp827CqHPpVX4NPXFUZb623K9N2GMVnvx3kSY1ZuZNy6hi+fOfFvEP7tufYrHkvCCF+UDsTY4wVBxfIjKmEiKr56fGpXqZun/Y2mAbEagr7jO0ugZof5YpuQ8dT/2FPq5yUseJzOh147K6m4kJOjosk+TfhsDwphEhTOxdjjN0MbrFgrIwREelketqoQeLTLXW9TjxrNt3bUHvZRXh6DWFGbwMt+f4t4bDZVEzLWMksmfO5YlegVHvmB61vQs/epNEfJlnzxMUVIBljzKvxCDJjZYiIIvz0mBflLzX9+V6jT/3ga3dQCCHQ6muLYoi7k557/TOewoKVG+lnkvHMoPbw7zcexhoJAADHuRNIX/ZBnisr9ahwWB8QQuxRNyVjjF0bv5NnrIxIRANNWhwc3UrXevtjPtctjgGAiPBlX4O09e/FdO5MchmlZOzWff72OLdcta5SUBwDgC6kJsIfmuYT2Hl4HGmNmySd4W0i0qqXkjHGro1HkBkrZUQU6KvDt4FG6vHzvSZTi8jiXXf3wC8W9xZnfbz13Sq+YI95vR3//Y13/jdCBI/4ijQmvyL3cedm4tzSdy2OtMPHhMN6lxDiWBnHZIyx6+IRZMZKERF189Hi8AONtb0OjDQXuzgGgPd6GOTTR/fJu7b8UwoJGfMcp8OOT994Tuib3nnN4hgAZHMgQgdNMQW0H9KANPrdRNIDZRiTMcZuiAtkxkoBEZl89fRFFSP9umigqcqMO4x6k7ZkbcRhZgkvt9cpX0x6wu3hmIx51MJZHytWl6IEth9yw32JJPi16CeHPfCuj+wX/LmkNy0gomtX1YwxVoa4QGbMw4iohVmHg7fX0Qw9/IzZ1L2O5paP+XxbvaRYM6Ulcz/3QELGPO9Mykks/mGG5HfH2GJ9TKILrY2IRz7zMdVv14e0hoNE1LK0MjLG2M3iHmTGPISItAYNJmoljP2yr9EwKK6EQ8bXsGCfE4/+5hJf/HmUdDpeoIx5l1efudd99Hwuhdw3ucQDL3mJ/+L879OsUNxThcvxphCCPzVhjKmCR5AZ8wAiqu+rw85WkfJzB0aajZ4ujgFgQKwGtf0V8cVbY/ldLfMqW9b9hUP7dkhV7nz5lv6m+NRvh4hHZhi1ITVfJJ3xXyKK9FRGxhgrDh5BZuwWEdHdJi1mv9PNYHyqhVa6dMEPT9ua4kan7y346OftqFI1vNTOw9jNstuseKJ/C0GN7iD/Nvd65JhCcePChvnO7E0LbcJlHyqE+NUjB2aMsZvEI8iMlRARkVFLr1Yx0g9rH/LxGdlSV6rFMQA0j5DRN1p2v/+/h/ijZ+YVFnz3geKARvFUcQwAJMkIaD9YW3XgFF/JFDBH0hm/ISKDx07AGGM3wAUyYyVAREZfHX6pGyS9sPtJH1PziLKboviD2w1y0qFd8r4dG8vsnIwVJeXUMSyb/5Xk32dcqfwCGKo1QORjX5gMUY3vJ51xIxGFlMZ5GGPsSlwgM1ZMRBTuq8OW7rU1t28e4eMT4Vu2v0YRvhKeb6NTZrz2KI8iM9UIIfDpG88pmuqNFX1EdKmdR9L7IOSeV4y+Te6IJa1hNxE1KLWTMcbYRVwgM1YMRNTMpMWesW310T/fZzQaPX8t3k15sb1ecuael39f8I0q52fsvzW/4fjhfVSl77hS/ztCJCGw80O6oO5PhJJGv5mIupb2ORljlRtfpMfYTZKI7jVp8f3MfkbTPbFateNg7h4nnvrTrXy5/Jik0dz6XMuM3SybNQ+P390Cmmb94deiX9meO2kPzi6cZBUux7PC7fqyTE/OGKs0eASZsRsgIjJpaUqwib5fN9zHK4pjABgUp0F1swtfvjOO3+WyMjX3y3fcLo3RXdbFMQAYohoh/MFpRtkU8KGkM04jIv47xhjzOH5hYew6iMjkq8Ov0VWkZ3c/6WNqEl52F+PdCBHhy74GacOfcynz/Fm147BK4vSJQ1i+aKYc0Pcl1X4ZtEGRCH94ukkbHDWCdMbfiMikVhbGWMXELRYAoqKiVpvN5ii1c5Sl3NzcpKSkpNvUzuHNiCjSV4eVveppaszsZzQaNOr0G9/IPT9Z3Ae18TTli9/4DS8rVUIIvDTiDiXZbaCQfv9T/RdCuJxI//1Dm/XoluPCYe0qhEhVOxNjrGLgxkUAZrM5av/+/XXUzlGWYmNj1Y7g1YiopUmLP19qr/d9ub1OU9rzG9+Kabcb5PqfbEPinq2o36i52nFYBbZ+xWKcOnmYQp6Y6RW/EKTRIrjvC4YLG+bVzd70824i6iaE2KV2LsZY+ccjToxdgYi6mLT4e05/Y+D/Oui9ujgGgOr+Ep5rrVM+mfgwT/vGSo0lLwdfvvcSjO2GkaTRqR2nEBEhoN392iq9nq1CWv2/RHSH2pkYY+UfF8hFkGUZCQkJiI+PR9OmTbFhw4bLtn/44YcwGAy4cOFC4X2//PILunb9/5mH1q9fj4SEBLhcrssee/78eXTp0gVmsxlPP/30ZdvGjx+P6tWrw2w2XzPbihUr0KxZMzRq1AjNmjXD6tWrAQB2ux09e/ZEXFwcZsyYUbj/Y489hh07dhT/SaikiOg2kxbLlt1vMt0V4x0X492M/3XQS9YL5+S/Fs9WOwqroH787E23ovdz+zXppXaUIvk06EChA9/wIb3PAiLpfrXzMMbKNy6Qi2A0GrFz507s2rULU6dOxcsvv3zZ9rlz56JFixZYtGhR4X39+/eHwWDAnDlz4HK58NRTT2HGjBm4cvotg8GAyZMn47333rvqvH379sXmzZuvmy04OBhLly7Fnj17MHPmTAwdOhQAsHz5cjRr1gy7d+/Gl1/mz3y0a9cuKIqCJk2alOh5qGyIqJuPFkt/H2wydalVvrqPfHSED3voxbxPXlGufFPG2K06eeQAVi2bKwf0+5/3XKVaBH1kDMKGvG0kvekbImmQ2nkYY+UXF8g3kJ2djcDAwMLbR48eRW5uLqZMmYK5c+detu/06dPxyiuv4NVXX0WLFi3Qtm3bq47n4+OD9u3bw2AwXLWtdevWCA8Pv26eJk2aICIiAgDQsGFD2Gw22O12aLVaWK3Wy0asJ0yYgEmTJhXr+62siKiHjxa//jHEZOpUs3wVxwUeiNdSuNGJbz4cr3YUVoEIIfDx5FGKtnYroatSXe04N6QLqVlQJH/LRTJjrKS4QC6C1WpFQkICYmJiMGLECEyYMKFw29y5c3H//fejQ4cOSExMxNmz/z+9Vu3atTFw4EB88sknePvtt0s958KFC9GkSRPo9Xp0794daWlpaNWqFcaNG4clS5agWbNmhcU0uzYiut2sw6I/HzCZOtQon8UxAEhE+KqvQVq3bBYuZKarHYdVEH//Pl+kpiQhqPdo727GvwQXyYyxW1V+q4FSVNBiAQAbN27EsGHDsHfvXhAR5s2bh0WLFkGSJPTv3x8LFizAyJEjAQCKomDlypUwm804efIkgoODSy3jvn378OKLL+Kvv/4CAGg0GsyZMwcA4HQ6cfvtt2PJkiUYM2YMkpKSMGzYMNx5552XHYOItAD+AXDtpmfvsAbAWCGEw9MHJqJeZh1+Xv6AydS2evn/dWgXpUG3mrL7g/GP4PUZv3r1x+HM++VmZ+HbDyeST6eHverCvJtRUCSn/fjit0QShFDmqZ2JMVZ+lP+KoJS1adMG6enpOHfuHNLS0nD48GF0794dAOBwOFC7du3CAvnTTz9FXFwcJk+ejJEjR2Ljxo0ojRkQTp8+jbvvvhuzZs1CnTpXz043Y8YMPPjgg9i4cSN0Oh3mz5+PNm3aXFUgCyGcRDQEgI/HQ3qOBGAygD+JaIAQIsNTByaiO8w6LFgx1GRsXa3i/Cp83MsgN/h0Ew7v34F6sdx/zkpu5ieT3MInCOZG3crlmy0ukhljJVVxqoJScvDgQbjdblSpUgUffvghXnvttcsu2qtVqxZOnjwJvV6PDz74AJs3b0ZISAi++uorfP3113j00Uc9micrKwt33HEHpk6dinbt2l21PTMzE8uWLcNff/2FJUuWQJIkEBFsNluRxxNCHPNowFJARHcDeAfARiLqI4Q47IFj9jXrMH/lUJOxVQUqjgGgRoCEp1vqlOkTHhYfL9xRLgsbpr6jB3dh3V+L5OAHP1Y7yi25okgWQijz1c7EGPN+3INchIIe5ISEBAwcOBAzZ86ELMuYN28e7r777sv2vfvuuzFv3jyMGTMG48aNQ0hICABg2rRpeOONN5CRcfWAZ82aNTFmzBh8//33qFatGvbv3w8AGDduHKpVqwaLxYJq1arhtddeAwAsWbIEEydOBAB88sknOHLkCCZPnlyY8dI+6EmTJuGVV14BEeH222/H1q1b0ahRI48X6mVJCOEWQowF8D6AdUTU6VaOR0R3+eowf/UwnwpXHBeY0FEv5WakyauXzr3xzoxdQVEUTJ88WtHVayu0gde/cLg8uKQn+TsiaaDaeRhj3o+XmgYQGxt7pBKupHd0//79ddXOUVxE1BXAHAAvCSG+K8Hj7/bV4cfVD/oYm0dU7MHVb3c4xPOrIb5afkySJH4vzG7eX4tnie8/fVOEPPm9JEkV502k49wJpP34olXYLcN5JJkxdj38V5OVK0KIVQA6ARhPRG8R0U3/DMsS9ffT44c1D1X84hgAHkrQUojeQd99NFHtKKwcyc46j5nTJ5G5y2MVqjgGeCSZMXbzuEBm5Y4Q4iCA1gDaAviZiG54keHF5aN/WPOgj6lpeMUvjoH8ad++7GOgNYu/Rc6FTLXjsHLiu49edcMv1O0T21HtKKWisEjWGb8jotvVzsMY805cILNySQiRDqA7gGwAa4nomhM+E1GsSYtflwwyGZtUkuK4QKeaGnSMktwfvDJCUTsL836Je7dh49+/yUF3v1Khf1F0ITVR9d7XjKTR/0xEDdXOwxjzPlwgs3JLCGEHMBzAzwD+I6Kr5jQjojAfLf7+7A6DubwtH+0pn/Q2yId2/isdO7RX7SjMi7ndbkyfPEroGnQSGr8QteOUOkO1WAT1fNqHtPrVRBSqdh7GmHepnBXDFXJzc5NiY2PVjlGmcnNzk9TO4Aki/yrTt4joMIC/iGiEEOJXACAis68Of49pow8cFq8rN6uAeVrtQAlPNNe6Pxr/ED5asLVCjwyykvvzl+9FRmaGCBn4VKUZODE37EKujOTA7K2/riCiVkIIq9qZGGPegWexYBUGEbUAsAjA0wCW+eqwvF+Mtu3MfgZDaSzYUp5k2wWiPszFQy9PR6fbB6gdh3mZrIxzeGpAK5h7Pgef6DZqxylTQgikL55qtZ7YsUI4rHcLIbgdiTHGLRas4hBCbAHQFMAGsw6fJ4TJrb+5k4tjAPDTE97urhOz339BURT++88u9/UH490UWM1d2YpjACAiBPd93qgJCO9KGv2baudhjHkHLpBZhSKEOKuTMaiqD92/bLDJpJW5OC4woomOAjV2mvXpZLWjMC+yf+d/2PrvCjmo3/hK235DGh1CB072Ib3pGZLkB9XOwxhTHxfIrEIhoq4GDd5aMdTH5Kfn4vhSskT4sq+BVv/8hcjNzlI7DvMCLpcTH08eJfQNu0PjW0XtOKqSTf4IHfSGiTS6z4ioYs5xxxi7aVwgswqDiOqYNFi0eKDJWDuQf7SLclstDdpUI2XaxMe5z4Lht/lfK9m5eSLgthFqR/EKuuAohNw93kga/VIiKncrjTLGPIerCFYhEJGfWYeV7/Yw+FTW6dxu1qe9DfKBbWulk0cPqB2FqSjjXBrmff2O5NfjGV6K/BLGWk0Q2HWED2kNfxNRkNp5GGPq4FdFVu4Rkeyrw6KBDbVhT7XQ8c/0DdSrIuORJlr3tP895FY7C1PPl+++5JaCaynGOs3VjuJ1fBN6yebG3UNIZ/yDiHRq52GMlT0uJli556PFO7EhcusZdxgMamcpLybfZpAz0k7K61f+qnYUpoLdW9Zh15Z/5KC7/8d/A64h8LYRen14/TjS6t9VOwtjrOzxiyMr14jodoOGnlh6v9Gk4xkrblqAgTC1q17MfHcMT/tWyTidDnzyxrNCH98bGlOA2nG8Fkkygu98wUSy7lEi6q52HsZY2eKFQli5RUTBJi0OLRlkCuxam/uOi8ulCNSbniea3vk0PfDk/9SO41FulwtWSy4seTmwWnJhzcuFzWYp3E4gyBoZGo0OGq0WWp0evv5B8PUPhFZbsT9R//n7acqied+I4Me+kbn3+MasJ3bi3MLJmcJlry+EOKd2HsZY2eACmZVLRES+OvzxcBNdl2k9DRW7oilFfx114Z4FNvH5b4lk8vFVO85NybmQgbNpp3Eu9TTOpZ3GmZSTjpSko7azqadEVsY5rd1m1SlulyxrtDaNRmuVZNkiSXIOESwAKYAgAARAIwS0Qih6IYTe5XT6OR12s1anc5l8/By+AYFu/8BgBAWHaavXijZGRNWh8Oq1EV6tJvQGk9pPQ4mcSzuNUfd3gH+/V2CsEa92nHIj8+9vHDk7//xXOKxdBf/RZKxS4AKZlUsaiUbUDpSm7XnSx0ev4daKW9FlZp6SF9kZL7/3o1cNJyqKgrTTx3H80F4cTdztStyzNe/k0QM6h91Ger0hTZLlk06H/ZDNajkE4CSAJACnAVwAkFeSQoaIJAD+AEIABF/8b6hWq4sxmHzi3W53PZs1L9zH7GcPq1bLVbNurD46rpmhflwzRETVhbePyE5+brD7YGo6qg56s9IuClISwu1C6vej85yZyeOFy/mR2nkYY6WPC2RW7hBRPZMWOzeP8DE1rMp/52/VwXQ3mn6Rh7dm/4PqNaNVy2G3WXBw91bs2rLWteO/v/OSTx4xabW6LI1GuzsvL3u94nZvB7ATwCk1R/GISAZQHUA0gFgfX/8uitvd2u12BdSs19DaqFk7c3TD5nL9Rs3gF+A9i29s37ga745/VIQ8+jXJxvLxaYE3cWamIPW7UVbhtLUWQuxWOw9jrHRxgczKFSLS+uqwfVIXfYNnW+u5OvaQJ5ZZ3X+dr473524os+fU6XTgyP4d2L1lnXvrv3/lnjx60GgwmBJttrylLqdzFYDtQoissspzq4goBEBLWaNtazSZu9qtefFVI6Icbbr08WnWtptcN7YJZFmdH1mH3YYn72kllJiuFNB+sCoZKoLcPatExorPTgqnraEQwnLjRzDGyisukFm5YtLSGy0j5dGrHzT5SMStFZ6SYRWoMS0HT73+LVp37l1q57HbrNjx32qs/XNh3o7/Vmu0Ov1Jp8P+m8NuWw7gXyFEbqmdvIxdnD+3rU5vuFOj1d2tuF1h8S07u1t16uXTrG1X+PqX3RoUc796R1m26EdR9bFv+E3lLRBCIH3xVKv1+Pa5isP6iNp5GGOlhwtkVm4QURs/PVYdHGk2hvt6d69neTTtP7t44z+t8vnvhzw6u4HNmoftG1Zh7fKFeTs3rdHo9cbduTlZ3wJYJIQ447ETeTkiqg6gp4+v/30Ou619g/hWru53DjE3b98DeoOx1M6blnwSzw7phMABk2CoFltq56ksFHsekr96wqLkZQ4RQixWOw9jrHRwgczKBSLy9dHi0A/9jWH9YrRqx6mQnG6BOh/niTYDnqNBj75wS8cSQmD/zv/w209fW7ZtWCnr9cbtuTlZ3wFYzFNl5f88A7jbx9f/SafD3qR15zuUrn3uN8Y2aePxNoxXnx7gPpphoZD7JvG7Sg+xJx/AmXmv5AiXvYEQIlntPIwxz+MCmZULfnqa07+B9u7v+xl5tbxS9PthJwYtdIgv/jxMhhJMZZabnYU1f/wkls77Mi83OyvLbre+r7jdPwgh0kshboVARBGSJN9vMPk8LklSZO97H9H17P+QJiAo5JaPvXndcnz42khR9fFvSdKVz6npvFXW+jnO7C2LtguHtZ0QgpdtZ6yC4QKZeT0iuifSl2YdfNpsMuu477g0CSHQ8XuL4qrVnca99f1NP9mH9m3HsvlfWTev/YM0Wu1yS17OBwDW8ZyxxUNEjYwm8/Mul/O+1p3vEP2GPGWsFR1XomPZbRY80b+loMZ9yL/1AA8nZUJxI23283mOcyemCJfjLbXzMMY8iwtk5tWIKNKkwYHVD5p8W1Xj1fLKwr6zbrT42oL3fvwX4dVrX3M/IQR2b1mHHz9/M/f0iUMWp8Pxgdvt+pZbKG4dEVXRaHVPyLI8tnqt+tp7Hhxtbt6+R7HaL2Z9OkVZ/vtCUfXRr/jCvFLizEpD6jcjLRdbLZLUzsMY8xwukJnXIiLy02Ptc631rV/rrOfG4zI0YonVvSa7Jt79Yd1VxZUQAlvXr8CPX0zNPZd6KtNqyf0fgHlCCJcKUSs0ItICuMdk9ptgNJlrDBs5waddt7tuuCBJStJRjBnWFUEDp0IfXq9swlZSWet+cGZvXbJasef1VDsLY8xzuEBmXouI7q4dSLMTnzb7aCRurShL6RYFNT/Kxeg3Z6F5ux4A8le2++/vZfjx86m5WZnpZ6x5OS8hfyYK7r8sZUREAG4z+fh+5OsfVPOhUa/5tOzYE1TEVIdCCIx/4i4lyQKE3DORL8wrZcLlQPIXI/LcuRn3CiH+UDsPY8wzuEBmXomIDD5anFhyvyn0tlrcWqGGd/+1K+9u04svfj8k79r8D75876W8CxnnTlrycl4E8Bv3F5e9i4XyHUaTeVpwaEToQ6NeNye06nxZobxh9VJ88sYYEfL4dyTp+JrWsmA9uhXnfn0rVThttYUQNrXzMMZuHRfIzCsZtfTqbbU0L/w22OSjdpbKyuEWqDktVyj+NZwXMs5l2Kx5TwP4hQtj9RGRBOAeg8nn/Wo16gU+8eK75tr1G8FqycPjdzeHtsW98Gt+p9oxK5UzC16z2JN2v6847RPVzsIYu3VcIDOvQ0TVjBok7nvKbKoVyJ8Qq+GCTWDC33b719sdis1NnwohxgshHGrnYpcjIlmSpEc0Wt17HXr01xKR7t9/14iqI77gC/PKmCv7LFK+etIqXPY4IcQxtfMwxm4NF8jM6/jr6ZenWuj6TO1m4AvzypgiBL7f6RRjlttsboGFuQ6MFUKcVTsXuz4iCjQYTW8rihhhaNoXAR2GUlH9yax0Xdj4k+vCfz+vEw5LV/6khXmbbdu2VdVoNF8DiAPAo0+AAmCvy+Ua0axZs6v+znGBzLwKEbWrYqS/Tj5rNvnwnMdl6kSWgvsWWPIOpivHcxx4WAixRe1MrHiIqAnpjD9og6rVCL5jjI82uLrakSoV4XYi+YvH8tw554YIIX5VOw9jl9q1a9eSsLCwBiEhIdmSJFX64k9RFDp37px/Wlra/vj4+Kt60vgdBPMaRCT76vDNx70MXByXISEEvt3hEHEzcq27zyiTcxxI4OK4fBJC7BAOa2PH2WP/S535bF7W+h+dQuFJRsoKyVpU6T3ah7T6r4iIly5k3iaOi+P/J0mSCAkJuYD8EfWrt5dxHsauSSY8XC9Iirw/jmetKCtn8xT0nmOxPPun7WieE61sLvE2T9tWvgkh3MLt+li47PWztyzemjZ7bJ7rAnfJlBVjzQQYajY1k0b3mtpZGLuCxMXx5S4+H0XWwlwgM69ARAE6Ge99dafRzL2TZWNJohPR03Ot/5x0f5HjQJwQYo/amZjnCCGShcPa3nHu5Bsp34y05h1cr3akSiOo++NGED1NRPXVzsKYNyGiZv369atVcNvpdCIwMDC+S5cudQHg448/rkJEzX799Vffgn1mzZoVQETNvvvuu0AAsNvt9NRTT0XWqFEjrl69eg0bNWrU4KeffvLzdFYeqmNewUeLKffEarVNw/ni+9KWYxd46nebbdEB54U8JwYIIbhyqqCEEAqAqUS08vzv0xZbj2wKCuox0sDzI5cujW8wAtoP0WX9O+9bAO3UzsNYUULCIuLTz6R6rA4MDg13nUtL2XW9fYxGo5KYmGjMzc0ls9ksFi1a5BcaGuq8dJ969epZ58yZE3TXXXflAMD8+fOD6tevby3Y/txzz0WkpaVpDx48uM9oNIpTp05pli9f7nvluW4VF8hMdUTUwEeLh9/trjeqnaWi25HqRu85FkueQyzKc+JJIUSO2plY6RNCbCGiGMvh/762Je3pE9L/FZM+rK7asSo032Z3ytlbFscTUWchxBq18zB2pfQzqZoaLy7z2PFOvt3npmrKrl27XliwYEHA8OHDM+fOnRt0zz33ZGzYsMFcsL1Vq1a5mzZtMtvtdrLZbHTixAl9w4YNLQCQk5MjzZkzJ+TYsWO7jUajAIDq1au7RowYkemxb+QibrFgqiIi8tPjy9e76HVVffjHsTQt3O9Eh+/yLGdyxUPZdvEAF8eVixAiR7FbBrpzzo848+O4vOxtS7nXvBSRrEFAxwd9SO/zrtpZGPMmQ4cOzZg/f36gxWKhAwcOmNq0aZN36XYiQseOHbN/+eUXvzlz5gT07Nkzq2Db/v379eHh4Y6goCCltHNyRcLUdkeAgZqMaqnj3opSIoTAa2vszgcXW8/nOdFBEWKB2pmYeoRQ5gqXo3HW2pmnzy//1M6zXJQen4adIWn1DYios9pZGPMWrVq1sp4+fVr/1VdfBXXr1u1CUfsMGTIkY968eUELFiwIevDBBzPKOiPABTJTERGRrw4ffdLL4KOV+cK80mB1Cgz4yWr9YKM9Mc+JRkKI7WpnYuoTQhwTTltC3v41W8/On2BR7Ba1I1VIJMk8isxYEXr27Jn16quvVh82bFiRxW+XLl0siYmJxoyMDE3jxo3tBffHxsbaU1NTdZmZmaVev3KBzNTUs6oPVe0Tza3wpSElR0GLr/LyVhxz/ZnjQEshRKramZj3EEJkCYe1iz310E+pM0dbXNk8FVxp4FFkxq725JNPpo8dOzalZcuW1mvtM2nSpNOTJ09OvvQ+X19fZdCgQemPPvpolM1mIwA4efKkdsaMGUGezsiVCVONvx5TJncx8LRupWBrihs9f7BY8pzibZsLU3jZW1YUIYSTiB52ZafvT/1u1OtV75tk1IdHqx2rQikYRc5Y9eW7AFqonScqKmq12WyOUjuHmnJzc5OSkpJuUztHZVanTh3nhAkTrvuu/L777ssu6v5p06YlP/vss5HR0dEN9Xq9MBqN7ldffTXF0xl5qWmmCiJqG+pDf50eY/bRSFwge9KyQ04M/NlqsTgxRAixWO08rHwgortIq59T5Y4xJp/6PDOZJwnFjeTPhue5czPuEEKsVTNLbGzskf3799dRM4PaYmNjj+7fv7/STeOya9euE/Hx8ekFt9WY5s0b7dq1Kzg+Pr7mlffzCDJThb8ekyd20pu4OPasJYlO3L/QmmNxopsQYrPaeVj5IYT4lYg6nF/2wUpht/ibG3fnFjwPuWIUuaXaeRgDgPJYzJYlfgFkZY6I4gC0GZ6g5erYgxYfLCyOb+PimJWEEGK7cNnbZKz4PCt394pSn0apMrnYixxLRJ3UzsIYuzEukFmZ89Pj1XHt9Doj18ces/CAUwzJL447CyG2qp2HlV9CiEThsrflItmzvHVGCyLC0KFDC2+7XC6EhISgT58+AIDvv/8eRIRVq1YV7rNo0SIQEX7++WcA+csFv/TSS6hXrx7i4uLQsmVL/PHHH1eda8iQIahfvz7i4uLw8MMPw+nMX0BNCIFRo0ahbt26aNy4MbZvL3qynYceegi1atVCQkICEhISsHPnTgDAwoUL0bBhQ3To0AHnz58HABw9ehSDBg269SeIVVpcILMyRUQ1XAr6jGzB8x57yoL9TjFskTXH4kInnsaNecIlRfIFLpI9xxtHkX18fLB3715YrfmTCaxYsQKRkZGX7dOoUSPMnTu38Pa8efMQHx9feHvChAlITU3F3r17sXfvXixduhQ5OVevQzRkyBAcPHgQe/bsgdVqxddffw0A+OOPP3D48GEcPnwYX375JZ588slr5n333Xexc+dO7Ny5EwkJCQCA999/H//99x+GDRuGOXPmAABeeeUVTJ48uWRPCmPgApmVMR8txj/VXCf5G3j02BPm73OKhxZbcyxOdBRC7FA7D6s4LiuS96zkq7k9wFtHkXv16oXffvsNADB37lzcf//9l23v0KEDNm/eDKfTidzcXBw5cqSwOLVYLPjqq68wffp06PV6AEBoaCjuu+++q87Tu3dvEBGICC1btsTp06cBAL/++iuGDRsGIkLr1q2RlZWF1NSbn5VSkiTY7XZYLBZotVqsW7cO4eHhqFevXkmeDsYAcIHMyhARVVUEHhjbVqdTO0tFMHevUzz8qzXb4kQHIQRfbME8TghxULjsbTP++iyLi2TP8GnYGZJGH0tE7W/lOJSvIxHNJqJNN/haS0S+1zrWoEGDMG/ePNhsNuzevRutWrW68lzo1q0bli9fjl9//RV33nln4bYjR44gKioKfn5+N53d6XRi9uzZ6NmzJwAgOTkZ1atXL9xerVo1JCcnF/nY8ePHo3Hjxnjuuedgt+evH/Hqq6/i9ttvx8qVK3H//fdjypQpmDBhwk3nYawoXCCzMmPU4LnBjbQUZuYfu1u1/IgLI5ZYcy4Wx7vVzsMqrkuK5AuWw5vUjlPukSTDr829JtKbXirR44mCiOhZAPsBfAFgG4DRAEZd5+sZALnXOmbjxo1x4sQJzJ07F7179y5yn4Iiet68eVeNMBfXU089hY4dO6JDhw4A8nuQr1TU/PhTp07FwYMHsWXLFmRkZODtt98GAHTv3h3btm3D0qVLsXjxYvTu3RuJiYkYMGAAHn30UVgsvFKktyCiZv369atVcNvpdCIwMDC+S5cudQHg1KlTmi5dutStX79+bJ06dRp26tSpLgAkJibqDAZD05iYmNiCr7Fjx4YX/FuW5WYF/54yZUpVT2Tlad5YmSAiX6MGT7/cXm9QO0t5tzPNjQELLBaLE72FEHvUzsMqPiHEQSK6PX3pO3+HDXnHpAut1NPo3jJzXFfKWvN9VyKKEELccIEDyq8W2wJ4HMCdAH67+O91nloE6M4778Tzzz+PNWvWFF7odqmWLVti7969MBqNiI7+/8Vk6tati6SkJOTk5MDX95qD1IVef/11nDt3Dl988UXhfdWqVcOpU6cKb58+fRoRERFXPTY8PBwAoNfrMXz4cLz33nuXbbdYLJg5cyaWL1+OHj164Ndff8WcOXPw448/4tFHH73xk1DJhEdExqelpnisDgwLj3ClpiRf99NMo9GoJCYmGnNzc8lsNotFixb5hYaGOgu2v/jii5G33XZbdsEiIps2bTIWbKtevbr94MGD+y893vvvv58KACaTqcmV224VF8isTGgkPNGzrkaqE8Sjx7fi1AUF3WZZrHkOPCSE+FftPKzyEEJsJpIePDP/lVnhw6cbNb7BakcqtyS9CT6xnZG7b/VTAF651n5EFABgKPKLYS3yR4zHCCHSr/WYknr44Yfh7++PRo0aYc2aNUXuM3XqVBgMl49xmEwmPPLIIxg1ahS++OIL6HQ6pKamYtWqVXjggQcu2/frr7/G8uXLsWrVKkjS//8tuPPOO/HJJ59g0KBB2LRpE/z9/QuL4UulpqYiPDwcQggsXrwYcXFxl21/5513MHr0aGi1WlitVhARJEniEeRrSEtN0fyyMc1jx+vfJuymasquXbteWLBgQcDw4cMz586dG3TPPfdkbNiwwQwAaWlp2h49elwo2LdVq1bXXIq6tHG1wkodEel1Ml6e2ElvUjtLeZZjF7htlsWS5xSvK0IsUDsPq3yEUH4WTvubZ+a9kqc4VPu7VSH4Nr/TQCSNJCLtpfdf7C1uTUTfATiO/JHjpwHECCE+KI3iGMgfxR09evR19+nVqxe6dOly1f1TpkxBSEgIYmNjERcXh379+iEkJOSq/Z544gmcOXMGbdq0QUJCAiZNmgQg/+K92rVro27dunj00UcxY8aMwsf07t0bKSn5g+xDhgxBo0aN0KhRI6Snp+OVV/7/vUVKSgq2bt2Ku+66CwAwduxYtG7dGjNnzsTgwYOL/4SwUjN06NCM+fPnB1osFjpw4ICpTZs2eQXbRo4cefaZZ56p2apVq+gXX3wx7MSJE4W/H6dOndIXtFEMHTq01JdL56WmWakjosFtq8uf//uwz40/f2NFUoTAHXMslvVJ7oW5DjzoqY9VGSsuIiLSGX/URza4q+qAV00k8YyNJZU687kcR9rhEUKIn4jIH8AQ5I8W+wD4EsD3QoiznjwnLzXNS00X3CaiZh4eQYYQYtv19jGZTE0sFsuOuLi4Bo8++ujZw4cPG3r27Jn9/vvvh/79999HAODMmTPyokWL/P/880//devW+e3Zs2dfTk6O1KdPn3qHDx/ed73jliT3tZaa5hFkVuoCDBg9upWOi+Nb8Mpqu+PfJPfBXAdGcHHM1CSEEMJhfciefGBf5t/fONTOU575tbrHl/Q+LxGRBsA+AJ0BjAEQLYR4x9PFMWPeomfPnlmvvvpq9WHDhmVcuS00NNT9xBNPZCxevPh448aN8/766y+zGhm5QGaliohqOt1ofFd9bncvqXl7neLjTY4LOQ70EkJwQcJUJ4RwCIe1Z+6uv87l7FrOC4mUkKleaxAQAyAaQJQQ4j4hxCohBD+nrEJ78skn08eOHZvSsmXLy3q1lixZ4puTkyMBQGZmpnTy5El9rVq1VPm7xwUyK1V6GSOGxWtJr+GFQUoiMd2NEUus1jwnevBoEvMmQogM4bR1y1z1pc2RnqR2nHKJZA3M8bdrSKN/nItiVpnUqVPHWTBTxaW2bNliSkhIaBAdHR3bsmXLBkOHDk3v1KmTKldZcg8yKzVEJPlocWb9wz7BCWHcp1hcTrdAwhd5eYfPKy853OITtfMwVhSSNSM0/qHTIh7+xIc0vAZQcTkzkpH63TM5wuUILotPiKKiolabzeZSv8DJm+Xm5iYlJSXdpnaOsnZlD7Ia07x5o2v1IPPn3qw03RbpJ+m5OC6ZCX/bHacuKFucCj5VOwtj16S4v3HnZfbLWP1Ntyo9ntSrHae80QZFQhscBUfakT4Afint81XGwpAVrTwWs2WJWyxYqfHX4+mnW+pUaa4v79addGH6Zoc1x4FBfFEe82YXL9obmrd3ZY7l6Ba145RLvs36+kp6n+vPscYYK1NcILNSQUT+Tmj6vL1RUcYutyHLxu11N+uCTeDeBVaLxYkHhBBn1M7D2I0IITKF094/fel7VlfuVRelsxsw1W8H4Xa1JKJItbMwxvJxgcxKyz0tYyLsbzzaXV6fWcUd/qEVcZ9b3Z9vtUNRuFi+nseXWa15TjFfCLFM7SyM3SwhxDq4XR+lL3knj683Kx5Ja4CpQUdA0jyidhbGWD4ukFmpCDQbHh95Z1PTgz0aYdMnD8qHvnsc9/duQ29s0omA9+yiy0yL8vdxl9oxvc68vU6x7JDrfK4Dz6idhbHiEi77BMeZo0dzti5xq52lvDE37mYgrf4htXMwxvJxgcw8jogi7E5XfJ/W/79QUfWqfhg/uK2UNOcpWv3uYIpuGCfuWuBC1Q9tytBfLDiZxSNOp7MVPLrUastzop8QIu/Gj2DMuwghXMJhHZC1brbDlVMqKyJXWPqIGEAo4URUS+0sjDEukFkpkIjuv7t9fcWgu3qSFCJC8/rh+OLZnnL6wtH4emxf6bwhyl1/hhW1ptvcE/+2weKonMXy07/bLC4F0260VCdj3kwIcRgCH2es+FyVuUvLK5JkmOq1ESDqp3YWxkqLLMvNYmJiYuvXrx8bGxvbYMWKFT6Xbn/99der6vX6pufPny+c/mrmzJkBbdq0iS64vXz5cnNMTEys0+ks1aw8DzLzuECzIXHBxLujuzWtedOPycyxYf6a/WLG0h3iSHKmFBMM99hWGvn+OA0kqeK/j/vnpAu9f7Scz3MiSgjBhQUr14jIRFrD8ZD+r1Q11kxQO065YTmyCed/+3CH25rTVO0srOK5ch7kamHB8clnzntsut/I0Cqu02np1506zmQyNbFYLDsAYOHChX5vvfVW+JYtWxILtjdq1KiBTqdTHnzwwfRRo0adL7i/c+fOdQcNGpTxyCOPZDRs2DD2008/Pdm9e3ePfNLK8yCzMkFE1XwM2hqd44s3D32grwFP9G1KT/RtSkeSM/H9X7tpzO+7xNPLHWgdoWBSZx21iKyYP65uRWDEEltenhMjuThmFYEQwkJEj57/fdqcyMe+8iGNVu1I5YKhRgIUp70hEQULIbhHhZWq5DPnNeJVP48dj14vXrF94cIF2d/fv/BipH379uktFov01ltvnZo6dWr4pQXyjBkzknr06FF/3759xoSEhDxPFcfXU/GH5lhZu6Nn89oujVzyH626kYGYMryTlPbTM7T0jXsppGZ9pfNsByKm2dyPLbUiLbditWB8td0pzuQpiQB+UjsLY54ihFii2C2bLmxeyFfj3iRJq4ehRrwDwB1qZ2GsNNjtdikmJia2Vq1aDUePHl3j1VdfTS3YNnPmzKD+/ftn9OzZM/f48eOG5OTkwoI7NjbWcdddd2V8//33IR999FFyWWTlApl5VKCvYdA9Her73HjPGyMitI+rjlkv9pXP//IsPnqmt3zYHe6u+bEV0Z9a3e/8a4fDVb6L5UyrwIsrbbZsO0bwgiCsohEOy4jsjQucrgtn1Y5Sbvg06GCWDOYH1M7BWGnQ6/XKwYMH9x8/fnzfokWLDg8fPrxWwdSvixYtCho2bFiGLMvo1atX5qxZswILHud2u7F27Vo/o9GoHDlypEzWtOcCmXkMERnzrM42tzf3/EXYBp0G93aKwd/vDZaTfnwKTw7oKH19wKQEvWdD62+syq8HS7dZv7RM+NtmVwQWCCF2qJ2FMU8TQhwHxLvn//qUW4dukrFOCyhOewciMqmdhbHS1K1bt7zMzExNamqqZtOmTcaTJ0/qe/bsGR0ZGdloyZIlQT///HNQwb5vvfVW1ZiYGOuMGTNOPP3001FlsZ4CF8jMkzo3rBlsD/IzlupJqgb64Ll7WtCh7x+XNn3yINq1SsDDvwtUed+m3D0/T+w7Wz4+0d1/zo1vdzhduQ48r3YWxkqLcDmm2k/ts9iTD6gdpVyQjX7QVa1lB9BN7SyMlaYdO3YYFEVBaGioa9asWUFjx45NSU5O3pOcnLzn7Nmzu9PS0nSHDh3SJSUlaWbMmBH68ccfnx4wYEB2WFiY88MPPwwu7XxcIDOPMRu099zXKcZcludsWDME7z/RVTq7YBTmT+gvaULqKs2/saP6Rzb3c39avXqJ6yeW2XJdCiYIIc6pnYWx0iKEsAmX45XMtTNz1c5SXvjEdvIlnel+tXMw5mkFPcgxMTGxgwYNqv3ZZ5+d0Gg0WLx4cdB9992Xdem+vXr1ypw5c2bQyJEjq48aNSotIiLCBQCffvpp0gcffBB+5swZuciTeAhP88Y8gojIbNSe/e/jYcENa4aomiXX6sCi9YcwY8l2ZefRM1LtQMk9srkkP9FM6zVTxq1PcqHnD5YzeU5UF0KUz/4Qxm4SEelIa0gOHTg5WB/ZQO04Xs914QxSvn4yR7gcgUIIXpWQeYQ3TPPmjXiaN1baGpr0WmNsjVL/1OOGzEYdhnaPw9DucdLpc9mYvXKv9PayneKlv61oUlVgYkcdda2t7o/+a2vsuRYnXuPimFUGQggHSfIrmWtnvhc2+K0y/ZSpPNL4h0L2DYYrM6UtgHVq52EVU3ksZsuSdwynsXJPlqjP3e2iNUSkdpTLVAvxw8v3t6UTPzxJf783hBo2bqT0X5i/xPWQhRYczyz7Fow9Z9zYeNrtFsD3ZX5yxtQilO8caUds3It8c3wadDSRRn+v2jkYq6y4QGYe4WfS39+vXbRe7RzXQkRoFh2GGaNul9MXjsa3L9wpXTBFuRt8lr/E9fjVNuSV0RLXk/6xW5xuvCOEsJXJCRnzAkIIB/ci3zxTdBsZkjRA7RyMVVZcILNbRkQBFrszprir56lFq5HRp3VdLHvjPjl1/jN4edht8m/JAUrI+zY0+dLqnrnTgdKaQuZ4poJlh1xwKvi0VE7AmDfjUeSbpq1aC1CUICIKVTsLY5URF8jME9ok1KlqM+jKX0t7oK8Bj92RgJ1fPCzt+eoR9LmtBb34jySC3neI23+wKJtOe3bKuLf+tdsI+EwIccGjB2asHCgcRf5nFo8i3wCRBF1YXTuANmpnYawy4gKZ3TKdRu7YtWlNj6yep6Y6EYGY/FBHKXX+M/TbG/dReO0Y0fVHJ8Kn2dyPLrEiJfvWRpXP5imYvcsJqwvveSgyY+WPUGY6UhKFM6NMVost1ww14s0kazuonYOxyogLZHbLzEZtj/YNq5XqfIRliYjQLq4avh/XR05fOBrTR/WWj4lwd+1PrKj3qc09dV3Jlrj+cKPDKUuYI4RIK4XYjJULF3vvv8zZttSudhZvp68WK5FW313tHIx50osvvhhWt27dhtHR0bExMTGxq1ev9mnZsmX9mjVrxhXMkdyzZ8/aADBmzJiIqlWrNi64PyYmJjY9Pb1M6o3y95k48ypEpNFppIatG0SqHaVUGHQaDOgYgwEdY+SzmXmY8/d+6fOlO5Q3/s2WGoaQ8mJbjdS/gfaGx7E6BaZvdrjznHijDGIz5tWEy/FJ7p4VIwM6PQRJZ1A7jtfSh0dDcdjqE5FOCOFQOw+rWKpFhMUnp57x3DzI4aGu0ylp1506buXKlT7Lly8P2LNnz36j0ShSU1M1drudAGDWrFnHOnbseNWy9E888cSZSZMmnfFUzpvFBTK7VY3DgsyOQF+D185g4SlVA33wbP8W9Gz/FrT/ZDq++3M3Hv1jNx793aZ0iHTT5C56ahRa9K/U4oMuaGVsFw5xrIxjM+Z1hBAnJIN5Q97+Nbf5JvRUO47XkvQmaPyCba6stAQAm9XOwyqW5NQzGrHiJY8dj7q/dcOaMjk5WRsUFOQyGo0CAMLDwz17oY8HcYsFu1VtO8dHVbo3WrE1gvHu47dJZxeMwk8T+ku60LpKy2/tqPaRzT36DysyLJe3YEzf7MjJsuFjleIy5nWEPe/DnG1Lc9TO4e301RvpwBfqsQqiX79+2SkpKbqaNWvGPfDAA1G//fZb4cJBw4YNq13QRvH4449XK7j/888/Dy24v1WrVtFllbXSFTbMswLNhh6dG0cZ1c6hFlmW0LVpTXRtWlPOszqw6N9D8oyl292R087ItQIl95PNJLlXXQ12pLklAL+qnZcxL/KnKyvN5Th3ErqQGmpn8VqGqDiD9fDG7gA+UjsLY7fK399f2bt37/4///zTd9WqVb4PPvhgnYkTJ54GuMWCVTBOt7tN24YVs/+4uHyMOjzQLQ4PdIuTk9NzMHvlXun9ZTvEy6stBIE/eGEQxv6fEMIlafVf5e7689mgbo/r1M7jrfQRMRBuF48gswpDo9GgT58+OX369Mlp3Lixdfbs2VXUzlQUbrFgJUZEkULAN7pakNpRvE5ksC9eGtSGjs9+knyMOpvNjWlqZ2LM2wiX48vcPSsV4fbaNkTVaQIjAJCJiKrdcGfGvNyuXbv0e/bsKbxmaceOHcZq1ap55QWoXCCzW9GmVUyEnYjUzuG1Nh9MhdXuygSwQe0sjHkbIcRRIinJdnqf2lG8FhFBHxHtAPchswogOztbHjZsWK06deo0jI6Ojj148KDx7bffTgEu70Fu27ZtYa/xpT3IMTExsYmJiWXyiRO3WLASM+k1HW9rUsNX7RzebNaKvXa7y/21EEKonYUxb6Q4rD9YEv8db6wRX+FnwikpQ40EX3vKwU4AFqidhVUckeGhrpuZeaI4x7vRPh06dLDs2LHj4JX3b968ObGo/T/44IOUDz74IMUT+YqLC2RWYka9tlWTOqE8fHwNQgjMX3vA7XC656mdhTGvJZRfLAfXvxjU/Uk9fxpVNH1kDJGs7ap2Dlax3GjO4sqOWyxYidkcrroNoryyt94r7Dl+DnanOxfAAbWzMObF9gu3M8d5lqcIvxZdeD0oDmsdIrrxqkSMMY/gApmVCBH5Ol1u3xqh/mpH8VpLNx5RhBCLuL2CsWsTQggoyk+WQxvdamfxVpLWAMlgtgPg+fAYKyNcILOSiomq6m+VJP5I9Fp++udgbp7N+YvaORjzdsJl/zlv/9qr5j9l/08bEO4GUFftHIxVFlwgs5Jq0KhWCP/8XEP6BQsST53XAVirdhbGyoEN7tzzcF0o87UAyg1tcJQeXCAzVma4wGElotPKcQl1Q33UzuGt/txyDCa9dr0Qwq52Fsa8nRDCDUn+zXL4P25HugZtleoG0hoaqp2DscqCZ7FgJWI2aFvERlXh/oprWPTvobzMXNtPaudgrLwQDuuv1qNb7/BrfhdPHVkETWAESKOLUzsHYyWVlpYmd+7cuT4ApKenayVJEkFBQS4AWLFixaGnnnqq+o4dO8z+/v4urVYrxowZk7ZixQq/LVu2mJ1OJyUnJ+tr1qxpA4AXX3wxdfjw4ZmlmZcL5JsQFRW12mw2R6mdw5Nyc3OTkpKSbivp411upT7PYHFt6/acBri9grHi2GhPTdQIIcDTvV1NGxgB4XbVUTsHqzhCwkPi09PSPVYHBocFu86lnrvm1HFhYWHugwcP7geAMWPGRJjNZvekSZPOKIqCpk2bxgwePPj80qVLjwPAoUOHdAsWLAiYPXt2EgAkJibq+vTpU6/g8WWBC+SbYDabo/bv31+hXphiY2NL/Fgi0mskqlovkpeYLkpyeg5yrA4AOKx2FsbKkSQobrvrwhmjNiBM7SxeRxMQCuG0hRCRRgjBa3OzW5aelq6J+95zH0rsfWhviWrKpUuX+mq1WjFu3LhzBfdFR0c7xo8ff9Zj4UqAe5BZSdQLDfSx6LSy2jm80oZ9yTDpNVt5ejfGbp4QQpCs3WxP5mnDi0IaXcFUb9XVzsKYJ+3Zs8fYuHFjr5vFhgtkVhINYmsEc/F3Dev2nHJk5dqXq52DsfJGseUut5/ayxe2XoMmIMwFnsmCVXBDhw6Nql+/fmxcXFwDNXNwgVwCb7zxBho2bIjGjRsjISEBmzZtAgB07twZ9evXR0JCAhISEjBgwAAAwGuvvYbIyMjC+xMSEpCVlXXVcceNG4eGDRuiQYMGGDVqFIoagPzggw8QGxuLxo0bo2vXrjh58iQAIDExEc2aNUN8fDw2btwIAHC5XOjWrRssFo+/MavZIKqK0dMHrShW7TxhU4T4V+0cjJVDG2xJu7lAvgZtcJQOQD21czDmSY0aNbLu3r3bVHB79uzZSWvWrDmUmZmpahswF8jFtHHjRixbtgzbt2/H7t27sXLlSlSv/v+feP3444/YuXMndu7ciZ9//rnw/ueee67w/p07dyIgIOCy427YsAH//vsvdu/ejb1792LLli1Yu/bqa7yaNGmCrVu3Yvfu3RgwYADGjRsHAPjiiy/w1ltv4eeff8Z7770HAPjss88wdOhQmEymq45zK0x6Ta0aof685GkRrHYnDidnGgFsVTsLY+XQDteFs0bFYVU7h1fSVqluJI1e1VE1xjytb9++OXa7nd5+++2Qgvtyc3NVr09VD1DepKamIjg4GHq9HgAQHByMiIiIWz4uEcFms8HhcMBut8PpdCI0NPSq/bp06VJY8LZu3RqnT58GAGi1WlitVlgsFmi1WmRlZWHp0qUYNmzYLWe7kkGnqR0ZzDMxFWXviXT4GLSnhBBe10/FmLcTQtglnfGQPfWQ2lG8kjYwAqTVNVI7B2OeJEkSli5denTdunW+kZGRjRo1atTggQceqPnaa6+dVjMXz2JRTD169MCkSZMQHR2Nbt26YeDAgejUqVPh9iFDhsBozO8+6N69O959910AwIcffogffvgBABAYGIi///77suO2adMGXbp0QXh4OIQQePrpp9GgwfUHCr755hv06tULADBy5EgMGzYMdrsdX3zxBSZNmoTx48ffcLokIooAkAcguxgXlVWPDDbf5K6Vy/6T6SCivWrnYKy8Ei7nOueZow2NNeLVjuJ1NIHhEIpSoWZUYuoJDgt2lXTmiWsd72b3/eCDD1IuvV2jRg3nsmXLjl1r//r16zsOHz6871byFRcXyMVkNpuxbds2rFu3Dn///TcGDhyIt956Cw899BCA/BaL5s2bX/W45557Ds8///w1j3vkyBEcOHCgcES4e/fu+Oeff9CxY8ci9//hhx+wdevWwjaMqKgorFmzpvBYKSkpiImJwdChQ+FwODB58mRER0dfdgwi0gH4E0AtADIRpQFIA5B68b9X/vuEECLd4XKHRlbhEeSi7DuR7rqQa+P2CsZKSLjse53nT1kB8HUOV9AEhEE4rGFERDxLDrtV15uzmHGBXCKyLKNz587o3LkzGjVqhJkzZxYWyCW1aNEitG7dGmZz/shsr1698N9//xVZIK9cuRJvvPEG1q5dW9jqcanx48djypQp+PjjjzFkyBDUrFkTr7/+On788cfL9hNCOAA0BgAiMgMIu+Qr/OJ/21zybxsRtZckCoiowiPIRdl+JM2iCJTZROaMVUBHHOlJDnCBfBVJawCIAAET8j/5Y4yVEu5BLqbExEQcPvz/6z/s3LkTNWrUuOXjRkVFYe3atXC5XHA6nVi7dm2RLRY7duzA448/jiVLlqBq1apXbV+7di0iIyNRr149WCwWSJIEWZZvOJOFECJXCHFECLFeCPGzEGK6EGK8EOIRIURvIURTIURbAAF6rezS6/i9VVEOJJ0nADyRK2Mld8SVmcovMNdAWoMTgL/aORir6PhFqJhyc3PxzDPPICsrCxqNBnXr1sWXX35ZuP3SHuTg4GCsXLkSwOU9yACwePFi1KxZs/D2gAEDsHr1ajRq1AhEhJ49e6Jv374AgIkTJ6J58+a488478cILLyA3Nxf33nsvgPzCesmSJQAAIQSmTJmCn376CQDw2GOPYciQIXC5XPjss8889RRUDfAxOABcPXRdydkdLpzJzDMCOKJ2FsbKsZOKLVcvXE6QhifLuZKkM7rc9rwAACk32pcxVnLEbUw3Fhsbe6QCLjV9dP/+/cWecJ6IOjSuXXXpri8e5hGMK+w9fg7tnp2dciHPHql2FsbKM0nvkxo+7P0wbRVeNO5KKd88dcGZntRLCLFR7SysfNm1a9eJ+Pj4dLVzeJtdu3YFx8fH17zyfm6xYMVVNSzQ5/pTY1RSJ85cgFYjn1A7B2PlHcnyMWcmD5AWRdKbAW6xYKzUcYHMiqtqRBUzt+YUITk9By63ckLtHIyVd8Ll3OPKTFU7hleSjGYJQIDaORgrKZPJ1CQxMVFnMBiaxsTExNapU6fh4MGDo9xuNxITE3VE1Gz06NGFC0ykpqZqNBpN02HDhkWVZU4udFhxBYYEmLj/uAin03NEjtXB/ceM3SLhtB1wnj9tA2BQO4u3kQy+GvAIMvOA8Miw+LSUMx6rA8MiQl2pyWk3PXVc9erV7QcPHtzvdDrRpk2b+j/88ENA69atLdWqVbP/9ddfAbjYZz9r1qzAunXr2jyV82ZxgXwTcnNzk2JjY9WO4VG5ublJJXyowceglT0apoJIOpttVRTBnwszduvOuS1ZTnCBfBXZ6KcFjyAzD0hLOaP5/Pg7HjveE7XGlaim1Gq1aNmyZe7hw4f1rVu3thgMBlG3bl3rP//8Y+rYsaNl4cKFQf369ctISUnReSzsTeAC+SYkJSXdpnYGb6HXyj4GLf/YFCU5PccF4IzaORirALIUa46idghvJBnMGkhyoNo5GPOUnJwc6Z9//vGbOHFicsF9gwYNyvjhhx+CIiMjnbIsi4iICCcXyMyraTWSj0HHA8hFOZNpEQDOqZ2DsQrggmLLVTuDV5IMPiCNPlTtHIzdqlOnTuljYmJiiQi9evXKuu+++7ITExN1AHDPPfdkT5o0KTI0NNR5zz33ZKiRjwtkViyyJPkYeJGQIp3PtkjgApkxT7igOCw8W04RSO8DkuQqaudg7FYV9CAXtc1gMIjGjRtbPvvss7C9e/fu/emnnwLKOB4XyKx4ZIlMXCAXzepwyQB42IuxW5cl7BZ+oSmCpPcBiILUzsFYaXvxxRfTOnbsmBMWFuZW4/z8AsSKhYiMer5Gr0hOlyIBKPMrbRmrgC4oLjsvo1cESW8ChOBZLFi55HQ6odPpbmqFuubNm9uaN2+u2t9ULpBZcRl5BLloTpdbBmBXOwdjFUAu3G6NcLtAMr/eXErS+0AIhQtkdsvCIkJdJZ154lrHu9E+W7duNVavXt1ev359x+HDh/dduf1a948aNeo8gPMeinpT+JWHFRcXyEUQQsDpVjTgApmxWyaEEKTRWRWHxSQb/dSOw1iFVJw5iz3hnXfeCfniiy+qvvvuu6fK8rwlxZUOKy4Dz2JxNZdbAYGEIpQbvoNmjN0YSZJTOB2AUe0k3kUobhCRU+0cjBXXuHHjzo0bN67cXMjOS02zYhFC6Hke5KvZHC7IMv/RYsxjhJBJ5jfjV3G7AIDfiDNWyrhAZsUiBHQ6vkjvKg6XAolIlSttGauIhBAyJH4zfqX8D6n4zThjpY0LZFYsRHA6nFwHXkmvlaEogv+aM+YpQpFJ4jfjVxKKGyBwgcxYKeMCmRULEdlsTv5070oGnQZOt6IlIl7cgDFPEELmGSyKkN9iwQUyY6WMC2RWXFarnQvkK2lkCRKRAMBztzJ2i4iIIBQZPIJ8FaG4AS6QWTlFRM369etXq+C20+lEYGBgfJcuXeoW3Dd79uyA6Ojo2Fq1ajWMjo6OnT17doAaWfntOSsui83BBXJRtBrJZXe6jQAcamdhrJyTARIkyfyJzJXyJ8rh1xh2yyJDQuJT0tM9VgdGBAe7ks+du+7UcUajUUlMTDTm5uaS2WwWixYt8gsNDS18w7dx40bj+PHjq/3111+HYmJiHAcPHtT16NEjOjo62t6qVSurp7LeDC6QWbEIISxWLpCLpNfK7osF8gW1szBWzmkhSQoAHkK+gnDzCDLzjJT0dM3++jEeO15s4sGbqim7du16YcGCBQHDhw/PnDt3btA999yTsWHDBjMAvP3222FjxoxJjYmJcQBATEyM47nnnkubOnVq2OLFi497LOxN4BYLViyKEBZusSiaTiMrAAxq52CsAtCBJL4auAhCcQGCFyRi5dfQoUMz5s+fH2ixWOjAgQOmNm3a5BVsO3TokKFVq1aWS/dv3bp13qFDh8r8bysXyKxY3G6Ryy0WRfMxat0AeNkvxm5dVUlv4jaCoihuAIILZFZutWrVynr69Gn9V199FdStW7fLPnEVQpAkXV6aCiGgxvXvXCCzYnG5lVxusShaWKBZAAhXOwdjFUC47BPEI8hFEG4XhBD85oGVaz179sx69dVXqw8bNizj0vujo6OtGzduNF163+bNm0316tWzlW1CLpBZMdldbh5BvobqIb4yuEBmzBMiNH7B/PepKIob4AKZlXNPPvlk+tixY1Natmx52YV3L774YtqHH34YnpiYqAOAxMRE3QcffBA+bty4tLLOyBfpsWJRFGGx2JwK+M3VVWqE+hvABTJjnhCh8Q/Vqx3CG+X3ICvcYsHKtTp16jgnTJhw9sr727Zta500adLpvn371nU6naTVasXkyZNPt23btkxnsAC4QGbFZ83NXylEp3YQbxMZbNb4GLQ11M7BWLkna6vLvsH8GlMExZYrhMtxRu0crPyLCA523ezMEzd7vBvtY7FYdlx5X58+fXL69OmTU3D7wQcfzHrwwQezPJWrpLhAZsWVeSYzzwEukK8SHmSGQaepqXYOxso7SauvLZuD1I7hlVwXztoglGS1c7Dy70ZzFld2/DE5K67UU+dy+OKZIoQHmSGEiFQ7B2PlHlF1LpCL5so+6wCQqnYOxio6LpBZcaWmns/l1a2KUCPUD3anmwtkxm6RUNyhGnMVtWN4JXfOeQBIUTsHYxUdF8isuFLPXbBwe0URoqr6w+F0+xKRWe0sjJVXRKQRTnuI7BesdhSv5LZkacEFMmOljgtkVlzpVodLa+ep3q4iSYSIYLMFQLTaWRgrx6Ilo59d0hnVzuF1hOKGcNj0AK66+p8x5ll8kR4rFiGEYjbqstMy8wJrhPqrHUc1h5Mz8NfW49i4Pxm7Tp5zJ2VmIzfPKSt2xQ9AGwDb1c7IWDnVWFe1tlA7hDdy52WCZG2u4rTxCAVjpYwLZFZsOo2cnno+t8IXyIqiYO+Jc1ix7QT+O5CC3afOuZOz8mC1OGQhAF2ITjFEGYSxuVEOjAhDWIQemf9mKunL0murnZ2xckvWNNVH1PdRO4Y3cudmgjTadLVzMHYrkpKSNE899VTUrl27TDqdTlSrVs0+ffr0Uy1btmxYs2ZNm9PppFatWuXMnj076ciRI7r4+Pi4mjVr2oQQMJlMyvfff388Pj6+1OcC5wKZFZskUUpqRm49tXN4iqIo2JKYipXbT2JLYgp2J6e707LyyGZxSSQTdFV1iqGGQRjbGeWQCD/oI/TQBGhARFe1KBlrGCXJILVR4/tgrCKQtMa2utDa3P5XBHduBkDE/cfMI6qFh8cnp6V5rA6MDAtznU5Nve7UcYqi4M4776w7ePDg88uWLTsGABs2bDCmpKRoq1evbj948OB+p9OJNm3a1P/hhx8CWrdubSm4HwDefffd4Ndffz38l19+OeGp3NfCBTIrNqfLnZSakad2jGJzOFxYv+801uxKwtZDadibku46l22V7BaXJOkJuqp6t6GGAYbORjksIjC/EPbTAMXo1TdUN0BxKLGl910wVrEJt6OhrmottWN4JXfueQjFfULtHKxiSE5L04jPZ3jsePTEUzesKZctW+ar0WjEuHHjzhXc17ZtW2vB0tIAoNVq0bJly9zDhw/rW7dubbn08dnZ2XJAQECZTDXLBTIrtlyr41jK+VwBwCune7PYHFiz6xTW7DqJbYfTxIEzme6MnPxCWDbJ0Ifr3YYaBjLEmTUREcHQR+gh+8gAIN/qubVVtICAkYhChBDnbvwIxlgBIgomjc4k+1VVO4pXcueeF8JuPa52DsZKavfu3cb4+HjL9fbJycmR/vnnH7+JEycmA8CpU6f0MTExsXl5eZLNZpM2bNhwsCyycoHMik0RSDl59oIVgEnNHNl5NqzYfgL/7DmN7UfSxKGzmUpmrk1yWt2kMWuEPkKvGGoayNDCT1Mtoip04TrIBs8UwtdCRNBH6K22k7bGAFaV1nkYq6DiNUHVrETEU0kWwXXhnBUQvIoeq5AKCmEiQq9evbLuu+++7MTERN2lLRZfffVV4MMPP1xj3bp1h0s7DxfIrCROHUnOdJbVyc5m5mHF9uNYv/c0th87I46eu+C+kGuXXXY3aQO0BYWwZGgXKEdF6KEP00PSSYRSLISvxyfGx2Q/be8ELpAZK64m+ohog9ohvJUr+6wTPAcyK8caNWpkXbx4cWBR2y4thK/l/vvvzxo1alTNUgl3BS6QWUkcOpKc6fGfnaSzF/DX1uPYsC8ZO0+cVY6fzxY5eXbJ7VBIF6RV9NUMiiHaIJu6VNEERuihC9VB0qhXCF+LbyNfbdb6rP4AJqqdhbHyRDL49jFENdarncNbXVxFj5eZZuVW3759cyZMmEDvv/9+8NixY9MBYO3atabc3NybutZnxYoVvtWrVy/1GSwALpBZyZzIyrPrLTYnTAZtsR986HQG/tp2cQ7hE2fdpzJzkGtxysItoKuiVfTVDYqhsUH2iwyRQiL00IXoQBJJKCcL25iiTVDsSj0iChRCZKqdh7HygIhkyNqWhqhGakfxSkIocOecNwLgHmRWbkmShCVLlhx96qmnqk+bNi1Mr9cXTvN2rccUtF4IIaDVasXnn39+siyycoHMik0I4Q4wG1IPJ2dUj68TWuQ+iqJgz/FzWLHtODYdTL04h3AurBanLASgr6pT9NUNwtjCKAdG5s8hrA3SlqtC+FoknQRjbaPNcshyG4CFaudhrJxoIvsEuGSfIj99rfRcF84CkpQrXCJD7SysYogMC3PdzMwTxTnezexXs2ZN5++//37syvsPHz6878r76tev77DZbKosvMUFMisRjUyHEk9nVG9UKwSbD6Zi1Y4T2JyYij2n091pF/5/DmF9qE7RR93cHMIViV8zP19bku0ucIHM2E0hoge0brspY8XnMCf0gi6khtqRvIrz7DGQRrtX7Rys4rjRnMWVHRfIrEQu5DpOP/DBMjFo6hKS9ARdaP4cwsYuJjksMhD68OLPIVyRmBuZ6ewvZ3sTEQkheNlcxm7AbDR2vK15I/n02UPuPbOXy1qtTlH8w6Gr20YyJ/SExlSxV+68EceZ44pit25UOwdjlQUXyKxEXIqy1hRpGlj32RoGT80hXJHow/UgLRlgR30AZTJnI2PlFRH5ajVyw5H39ofJoJddbjcOHD8pbd53QPyzY7VycsMcyWAyu93BtSVTg85katABkqZyzQRnTzmYC8WlykfNjFVGXCCzktrhzHA6ZB+Zp2QqAhHBt7GvlLUh63ZwgczYjXSNqVHDZjLodQCgkWU0qlsbjerWpkfuuoMsNht2JB6WN+7Z597w3/fSmRXTSWcOdCvhDWVz/O0wVoIL+xxnj0sA+CNxxsoIF8ispA46M50mxaVA0lTKLoob8k3wNebsyhkA4CO1szDmzUwGff/OzRJ8r73dgHbxjdAuvpEMAOcys7D1QKK8fuce99bFk+QcRRHkV1WRazaTfZvcAW1geNmFLwOK3QLFlqMHcETtLIxVFlwgsxIRQthkk3zWkeaIMFTjQeSimBuaodiVFkRkEELY1M7DmDciIjLodH3aNG5400vXhwQGoFfbVujVtpUshMDxlFTavO+A9M+O3e4D3y6TdXqDogREQh/dTvJt3BOSQdVFP2+Z49wJSDrjMbc1x612FsYqCy6QWYmRRHtsSTYukK9B9pGhC9PZ7aft7QGsVDsPY16qmY/RoKseWrVEDyYi1I6MQO3ICBrUo6vsdLmw9+hxadO+A2Ldjt+V5H9mSgYfP7c7uI7kE3cbGeu3hSSVrz99zrPHIRRlq9o5GLtVsiw3q1evnlUIAVmWxUcffZTUvXv3vILtr7/+etU333yzWkpKyq4qVaqo+oawfL1KMK/iznOvshyxdAloG1C5rpYpBv9W/j7p6ekPgAtkxopk0OtG9O3Q1kB00wPI16XVaNCkfj00qV+Pnuh/J+VYLNh+8LC8Yfde939rP5fO/PEhaX2D3IhoJJub9IQhIsYj5y1N9rTDVuGw/Kd2DlaxhIdGxKedTfVYHRhWNdyVeiblun3yer1eKVhOeuHChX7/+9//qnXv3j2xYPvPP/9cJS4uLu/HH38MGDVq1HlPZSsJLpDZrfgv72CeDQAXyNcQ2C5QPrfk3L1E9KQQwqp2Hsa8CRHp9Frt4F7tWpfaLDi+JhM6NY1Hp6bxMgCknc/A1v0H5X927Hbv+Gm8nE2SgF9VRVu7pezbpDc0fiGlFaXEHKmHHeAL9JiHpZ1N1Xzy+CqPHe/pL7oWq6a8cOGC7O/vX7i4yL59+/QWi0V66623Tk2dOjWcC2RWnm2zn7EbFYcCSccX6hVFG6SFsZbRbTlk6Qdgrtp5GPMyPWuEhyIyJLjMThhWJQh9OrRFnw5tZUVRcOR08sX+5S3uw1/9IusNJsUdGEWG+u3J3KgbJJ2xzLIVRQgFzswUE4A9qgZhzAPsdrsUExMTa7fbKT09Xfv7778fKtg2c+bMoP79+2f07Nkz97HHHjMkJydrIiMjb2p1vtLABTIrMSGERfaRT9hO2uqZ6pXvi2BKU9BtQb6207ZnwAUyY5cxG41P3d254zVnryhtkiQhOqo6oqOq0wO9esh2hwO7jxyTNu3dr6zf+YtIW/ONpPfxc7ur1pd8GnUlY52WkKSyHQxwZaaCJPmC4nJklemJGSsFl7ZYrFy50mf48OG1Dh06tE+SJCxatCjol19+OSLLMnr16pU5a9aswJdffvmcWlm5QGa3xo21lqOWuqZ6Js80EFZAfk39kPxtchMiqi6EOKV2Hsa8ARFV0Wk1nbs0b6J2lEJ6nQ4tYmPQIjZGevq+/sjKycW2g4fkDbv2ujetmCadXeYgjW8VN1VPkM0JvaAPrV3qmRxnj4NkXmKaVTzdunXLy8zM1KSmpmpOnz6tPXnypL5nz57RAOB0Oql69ep2LpBZuaXYlbV5B/MGBvcMVm0UyNtJOgkBrQOQtSHrEQCvqZ2HMW8gS9Lw9vGN3GaTui0M1xPga0bXFk3RtUVTWQiB5LPp2HrgoLx2x2737jnPy7KsEcIvTNHVbSWbE3pBYw7yeAZ78gGnYs9b4/EDM6ayHTt2GBRFQWhoqOvNN98MHTt2bMrUqVPTCrZHRkY2OnTokC46OtqhRj4ukNmt+s9y2MLLTN9AULcgQ9Z/WaOIaIoQQrWeKsa8ARFJRr1+zH3du5Sb3iwiQrXQEFQLDUG/zh1kt6Ig8URSfv/yzvXuY5t+kg1GH8UVVJNMMZ3IJ66LR5bDth7baoVQPHclFWMqKuhBBgAhBD777LMTGo0GixcvDlq2bNnhS/ft1atX5syZM4PeeOONtKKPVrq4QGa36qjiUBRnhhPaIK3aWbyWMcoIfbheYztpuwvAQrXzMKayrlX8/cwNa9dSO0eJyZKE2No1EVu7Jj3Ut5dstdux6/BRaePufe5/t86R0lZ9RjpzgFuENZDNjbpDXzOh2P3Lii0XrqwzBgCbS+e7YJVZWNVwV3FnnrjR8W60j9vt3lbU/cnJyVddhPr111+f9kSukuICmd0SIYTQmDXbLUctHf2D/NWO49WCewX7psxKeRFcILNKzmw0Pj+oR1ezp+Y+9gZGvR6t42LROi5Wfm7wvTh/IRvbDiTK63ftcW/57S052+2GZA52yzWbyuYmvaGrUv2Gx7Sd2gdJZ9jptuao8hEzq9huNGdxZccFMrtlbot7We6e3Jb+Lfx5Sb3r8Gvuh5SZKXFEFCuE2K92HsbUQETV9Dptxx6tW1Sc6rgIVfz90KN1C/Ro3UIWQuBk6pn8/uXtu9z7Zv4ha7V6RfGPgK5eW8kcfzs0Jr+rjmE7scOh2C1LVIjPWKXHBTK7dQJ/5uzMmah2DG8naSRU6VZFe/6v888DeFjtPIypwaDTje3ZtiWZDHq1o5QZIkLNiDDUjAjDgK6dZZfLjf3HT0ib9u4X63atUJL+/UEy+Pi63VVqS6aGXcjUoAMkSQPrsW02CGW12vkZq4xICKF2BlbOERGRjjLqTq4boA+tPH/0SsKV7ULi84k24RCxQojjaudhrCwRUVWdVnti3hsTjVWDAtWO4zXyrFbsSDyCjXv2uTfs3itdyM0jnTnAlXvhvAJFMQshnGpnZOXfrl27TsTHx6erncPb7Nq1Kzg+Pr7mlffzCDK7ZUIIIZvk5bl7cgdygXx9Gj8Ngm8P1pxfef5tAPepnYexsmTQ6cbf3roFcXF8OR+jEe0TGqF9QiMZAM5mZGLW739pVm7eui8nz8LFMWMq4PWBmUcoVmVx9vbsHLVzlAfBvYM1EOhDRHFqZ2GsrBBRVUWIRx/q24uvVbiBqkGBsNrt1jyL9Uu1szBWWXGBzDxlpeWwRa+4FLVzeD3ZKKPqXVV1klH6QO0sjJUVg043vlfbVhQSGKB2FK8nhMDG3fuEAJarnYUxTyKiZo8++mi1gtsTJ04MHTNmTAQAjBkzJmLixImh6qW7HLdYMI8QQqTLPvIJ6xFrtE+Mj9pxvF5Q1yD53G/n2hFRayHEf2rnYaw0EVG4Qad7ikBSntUKH6P3rp7nDY6npMHpclkAHFI7C6u4IsIj41PTUjxWB4aHRbhSUpOvO3WcTqcTv//+e2BqampaePiN501WExfIzGOEQ/ySszvneZ8YH/65ugFJJyH03lBj2ry0j4moleCrZVkFptfIU+sGB4oNO3aKv/7bjGF9eir3du0k6XW3vtJcRbRp735BhN/5dYGVptS0FM2/i/d57Hjt+jW84d9+WZbFsGHDzr355puh06dPT/bYyUsBt1gwjxEu8Xv29myL2jnKi8D2gSQZpFgA3dTOwlhpIaL6Arjv3haNteN6dpTvax6HBX+tFP2efwWL/l4nXC632hG9zvL/NudYbPb5audgrDS88MILZ3/55Zeg8+fPy2pnuR4ukJkn/ec875Qc53nRp5tBMiH8/nAfyShNp4q0pBhjl9BrNB91bVBX56PPHy2OiwzDy706y70b1sU3i5cq/ce9IpZv3Ay3wtcvAMC5zCwkpZ7RAFildhbGSkNQUJBy7733nn/rrbeqqp3lerhAZh4jhHCSTL9mb8nmv3Q3ya+5H7QB2kgA96idhTFPI6JeOo3coWO9WleNFLWoVR2v3NFFblerGk2bu0AZ9PJr4p8du1DZuwrWbt8ltFrNH0IIu9pZGCstL7/88pk5c+YE5+XleW0d6rXBWPmk2JSZmesyc9XOUV6QRAgbHGaWDNKHRMQNmazCICKjTiN/O7BFY5NWc+1PUjvVr40JvTtLDUOD6I1vZothr76hbD2QWIZJvcufGzfl5Flts9TOwVhpCg0Ndfft2zdzzpw5wWpnuRYukJmn/e0465Ac57jN4maZ48ww1jYGkpbGq52FMU/RytJr9aoG+8WE3/hTVEmS0DOuPib06UIRJoP08idfisfeeE/Ze7RyLTaZcSEbR0+n6AD8pXYWxkrb+PHj07Kysrz2on5eapp5nGyUZ4bcGfJASO8QfgN2kxznHTjyvyNWxa40F0LsVzsPY7eCiBroZHnbi707G/2NxV8XxOFyYeG2vWJf6jmKq1PL/czA/nKdapGlkNS7LF6zDp8vXPJrjsXST+0srOK5cqlpNaZ580a81DQrM4pNmZW1PqtfSO8QP7WzlBe6KjqEDgzVn/npzHwiShBC8KX9rFwiItJrNLN6xkXrS1IcA4BOo8H9rRLI4nBg/ubd0mNvvIdWcbHupwb0k6uFhng4sff4Y8Om7FyrldsrWJkoj8VsWeIRPlYa1jrSHcRtFsUT1DlI0kfoa5FMo9XOwtgtGBpgMjRoV7fmLf99Mel0GN6+OY27vSNSU5LpwdfexJvfznafy8zyQEzvkpmTg0NJp3UA/lQ7C2OMC2RWCoQQLpJo4YXNF3g2i2IgiRDYPtBH1srvEFG02nkYKy4iitTK0if3t0zwkSTPzVzobzLg8U6tpGe7tcW+Q4cw8H+vY9qcBe6snIpzPfD6HXug12lXCyF4LnnGvAAXyKxUKDZlVua6zDy1c5QXiktB0kdJSvqCc2jUOYYMPvqFRMQtUKzcICJJr9H81CWmjqFakH+pnCPE14xnbmsrP9W5FTbs2Il7XpyArxYtVfKs1lI5X1n69Z/1ObkW67dq52CM5eMCmZWWf5wZTrct2aZ2Dq+Xsz8Hx8YeVQJcvpjw5xg8+ukDUrXYiFpavWai2tkYu1myRM9WMZviuzaoqy3tc1UL9MeY7u3l4W2b4Y/1G8Tdz7+CH/5Yodgd5bOtK/nsORxLTiUAS9XOwhjLxwUyKxVCCDcEvspck1k+/2KVAcWl4NSnp0Tq9BTcMbIbXlr8jBQSVQWSJGHE9ME+slZ+nohaqZ2TsRshogSZpCnD2jb1kaWy+7NSt2oVjLu9/C9fvWz9RpdENFsIwa+XjHkJnuaNlRoiqisZpN0xn8QYJQ2/F7tU3qE8pM5IUYJCA/HYpw9IYXWunit22++7MeuFn5LtFkeMEKLiNFuyCoWIzDqNfGBAs0aRTWtEqrpk+pbjp7B8/xG3JMvSyHvvpm6tmqMsC/aScCsK7hzzsuVCbl5bIUSpzyoQFRW12mw2R5X2ecqL3NzcpKSkpNvUzlEWrpzmray1bNmy/osvvph6zz33ZBfcN2nSpKqvvvpq9fr16xf2Sbndbjpy5Ihh27Zt+5o2bVrqH0PzNG8lVFFfTMriRUEIcUT2kQ/k7Mhp6t+idHoSyxtFUZD8dbKwbLdQ76dvw+1PdJEkueg/4M16N8bevw9W2fHHnp+JqLcQgi96ZF5Hr9F8FxcZGqx2cQzkL1/dolZ1eW3iMUybs0D5+tffaNTAe6h9QiMQqR6vSNsOJMLldqcA2E9EfkKI7Bs+6BaYzeao/fv31ynNc5QnsbGxakdQTVho1fgzZ895rA4MrRriSjtz9ppv8u69997zc+fODbq0QF64cGHQH3/8kdizZ8/CQaCnn346MjY2VlcWxfH1cIF8AxX1xaS0XxSISAYQCGDa+RXnZ/i38DeX6gnLActxC1I+SXH7+/tKoxY/gsj6YTcc2hryRn9DyqG09qmHz7wN4IUyiMnYTdPI8tP+RkPve5o1KtmEx6WkU/3a6FCvpvTX/sOY8s0sUTUoQIy+/16peYP6ake7yoJVa/IsVtv7AFoBWEZEfwL4GsBqflPMStOZs+c07913h8eO9/xPv123phw6dGjmm2++GWm1WsloNIrExETd2bNntT169Cgsjv/44w/zkiVLAnfu3Kn6glne/dkTK3eIqBoRTQRwDMB7AH62HreS42zlba1TFAXJ3yeLU2+fwm2D29HEP8dQZP2wm3qsRqfB098+7GMwG56SNdKwUo7K2E0jou5aWXrnsU4tTTqNrHacq1y+fLXeK5evPn8hG9v2J0oCmCOEWA+gFoD1AN4HcJSIXiGiauqmZMwzwsLC3PHx8XkLFy70B4CZM2cG3XnnnZnSxTao9PR0+bHHHqv59ddfHw8KClL9zSEXyOyWEZGGiPoS0RIAuwGEAegnhHhICGEF4fuMvzOcKsdUhe2UDcfHHXPLxwgv/PwU7hxzuyRri1dM+FYxY/TsR01avfYzImpTSlEZu2lEVF8rywuHt29uDDb7qB3nujSShLubxmHCHZ3JoDjp2fen49n3p7uPnk5WOxp+W7/RrdHICwvaKoQQmUKITwAkALgXQDUAu4loGRH1IyKPzxBy+vRp3HXXXahXrx7q1KmD0aNHw3FxNpCdO3fi999/L9z3tddew3vvvefpCJf59ddf0bhxYyQkJKB58+ZYv359kfs99NBDqFWrFhISEpCQkICdO3cCABYuXIiGDRuiQ4cOOH/+PADg6NGjGDRoUKnmZjfnvvvuy5g/f34gAPzyyy9BQ4cOzSjYNnz48KgBAwZk9OjRwyumiOUCuRjOnz9f+MsYFhaGyMjIwttnzpzB4MGDUbt2bTRr1gxt2rTBokWLMHLkSCQkJCA2NhZGo7Fw/59//vmq4//000+IjY1Fw4YNMXjw4CIzzJ8/H40bN0bDhg0xbty4wvunT5+OuLg49O7du/DFbf369RgzZkzpPBkAiCiKiF4HcALA/wAsAlBdCPGUEGJHwX7CIaZnrMlwKS7V3xCWqdQ5qTj5xkm079+KXv3reYpqGFniY0XWD8OI6UNMOoP2dyKq4cGYjBULEQXpZHlVvyaxPnVCqqgd56bpNBoMbpVA/+vdCdYLWdJjb7yH8TO+cp8+c06VPIqi4OdVa2wWm/2jK7eJfFuFEE8AqA7gZwDPA0gioreIqJ4nMggh0L9/f/Tr1w+HDx/GoUOHkJubi/HjxwO4ukC+VW73jWcX6dq1K3bt2oWdO3fi22+/xYgRI66577vvvoudO3di586dSEhIAAC8//77+O+//zBs2DDMmTMHAPDKK69g8uTJHvke2K0ZMmRI1r///uu3fv16k81mk9q3b28BgOnTp1c5deqU/p133klRO2MBLpCLoUqVKoW/jE888QSee+457Ny5Ezt27EC/fv3QsWNHHDt2DNu2bcO8efNw+vRpfPrpp4UvMnXq1Cl8/IABAy479uHDhzF16lT8+++/2LdvH6ZNm3bV+c+fP48XXngBq1atwr59+3DmzBmsWrUKAPD1119j9+7daNKkCZYvXw4hBCZPnowJEyZ49DkgIu3FkYzfAewAEASgtxCijRDiOyHEVe/8hBCJIOzN3lqq1554DVuqDUfHHXEre1xizJzHcc/Ld0ha/a23+ze6rQH6junhqzfpVhJRpe/pZmWPiLR6jWZZy9rVg1vVjiqXfz8uXb46JVm95as37N4Lu9N5Sgix9Xr7CSHyhBDfCyHaA7gN+dcO/UtEa4loKBGZSpph9erVMBgMGD58OABAlmV8+OGH+Pbbb5GdnY2JEydi/vz5SEhIwPz58wEA+/fvR+fOnVG7dm18/PHHhcf64Ycf0LJlSyQkJODxxx8vLIbNZjMmTpyIVq1aYePGjXjppZcQGxuLxo0b4/nnn78qk9lsLrygMi8vr9gXV0qSBLvdDovFAq1Wi3Xr1iE8PBz16nnkPQW7Rf7+/krr1q1zRowYUbN///4ZALB//37dlClTIufMmXNMqy31adRvWrl8gfM2q1evhk6nwxNPPFF4X40aNfDMM8/c9DG++uorjBw5EoGBgQCAqlWvnvbr2LFjiI6ORkhICACgW7duWLhwYeF2p9NZ+KIwe/Zs9O7du/B4t4qIahHRGwBOIn8kYx7yR4ufEULsvtHjFYvy5rkl53Iq+rSCaQvScHLSSbTu1Qyvrx5HtZp4dgKUbiM6ygm3x1XT56+0x7+/rMwQEek08hfVg/zj74yP1aud51apvXz1t0v+yMmz2l4rzmOEEAeEEM8jv/XiYwCDAZwios+I6MXrfI0mIt2Vx9u3bx+aNWt22X1+fn6IiorCiRMnMGnSJAwcOBA7d+7EwIEDAQAHDx7E8uXLsXnzZrz++utwOp04cOAA5s+fj3///Rc7d+6ELMv48ccfAeQXuXFxcdi0aRNiY2OxaNEi7Nu3D7t378Yrr7xS5Pe5aNEixMTE4I477sC33157ccHx48ejcePGeO6552C32wEAr776Km6//XasXLkS999/P6ZMmeLxgSJ2awYNGpSRmJhoLGivmDJlSrjVapX69+9fNyYmJrbg688//1R1IIhnsfCAffv2oWnTprd0jEOHDgEA2rVrB7fbjddeew09e/a8bJ+6devi4MGDOHHiBKpVq4bFixcXtlM8//zzaN26NRo2bIh27dqhX79++PPPP697zosF1iAA12si1AG4E0AzALMBdBNClOTq0iXODGe2JdHi6xPj3T2LJeE458DpD065tW6NNHrmCKrbolapXLVERBj61gDDmWPn2qUkpr0LYGxpnIexK2llabKfwXDfQ+2amSTJO6dMK4mC5atPZ17AT1t3Yun6DRjU/TZlcM9uko/RWCrn3Hv0OE6dOWMFsPCGOxfh4oIiCwEsJKIoAPcj/9O8a7GhiAExIUSRI7TXuh8A7rjjDuj1euj1elStWrXwk8xt27ahRYsWAACr1Vo4yCPLMu655x4A+cW3wWDAiBEjcMcdd6BPnz5FnuPuu+/G3XffjX/++QcTJkzAypUrr9pn6tSpCAsLg8PhwGOPPYa3334bEydORPfu3dG9e3cAwMyZM9G7d28kJibivffeQ2BgID766COYTCUedK9QQquGuG4080Rxj3cz+w0bNixr2LBh2wpuz5kz5yTyB9+8ChfIpWDkyJFYv349dDodtmzZclOPcblcOHz4MNasWYPTp0+jQ4cO2Lt3LwICAgr3CQwMxGeffYaBAwdCkiS0bdsWx44dAwAMHToUQ4cOBQC8/vrrGDVqFP744w/MmjUL1atXx/vvvw/p6gnzJQAtcf0CWQCYCeAuIUSJ5yQUQihE9NrZxWc/rPVSrQrVHnD217PI+jMTLfs1wb2v3El601UDNR5VMLPFlDs+fEKr16Q57a53S/WErNLTyvJYH73uuZG3tTEZvOgjUE8qWL76yNnz+GX9BrFg5RoM69NTubdrJ0mv8+zv9Kzf/sxzOF1vCiFuqqC4HiFEEoC3S/LYhg0bXvYpJABkZ2fj1KlTqFOnDrZt23bVY/T6///wQJZluFwuCCHw4IMPYurUqVftbzAYIMv54wUajQabN2/GqlWrMG/ePHzyySdYvXr1NfN17NgRR48eRXp6OoKDgy/bFh4eXphn+PDhV108aLFYMHPmTCxfvhw9evTAr7/+ijlz5uDHH3/Eo48+eoNnpnK43pzFjFssPKJhw4bYvn174e1PP/0Uq1atwrlzN3/xR7Vq1XDXXXdBq9WiVq1aqF+/Pg4fPnzVfn379sWmTZuwceNG1K9f/6q+qpSUFGzZsgV33XUXpkyZgvnz50Ov1xf2Kl9KCOESQjwrhHj0Ol+PCSHm3EpxfIkfLMcsLtspVef+9hhHhgPHxh9zW/7Jw1NfPYQH3hwgl3ZxXMAc5INxC582+QSYXtPqtaV3JSar9GRJfk0rS1Ofvq2tyddQ7jsrbujS5at/urh89eI1nlu++vSZc9h24JBwK8o3HjngLejatSssFgtmzZoFIP8iurFjx+Khhx6CyWSCr68vcnJybuo4P//8M86ePQsAyMjIwMmTVw8I5ubm4sKFC+jduzemTZtWOPPEpY4cOYKCVrzt27fD4XCgSpWrLwZNTU0FkD/avXjxYsTFxV22/Z133sHo0aOh1WphtVpBRJAkCRaL5YbfD2MAF8gecdttt8Fms+Gzzz4rvK+4v4T9+vXD33//DQBIT0/HoUOHULt27av2K3gByszMxIwZM666wnfChAmFV+t624uCEMIGBe+dXXpW/TC36Nwf53DileNo1DIGk/9+ETHtyv4CkKCIALz4y9Mmk79xskanGVXmAViFp9FoHzIaTS+6FaE9cja9Yl9AcIW4yDD8r1dnuXfDuvh60VLlnhcniL/+2wK3cmuz8cxZvsImgBnesHw8EWHRokVYsGAB6tWrh+joaBgMBrz55psAgC5dumD//v2XXaRXlNjYWEyZMgU9evRA48aN0b1798IC9lI5OTno06cPGjdujE6dOuHDDz+8ap+FCxciLi4OCQkJGDlyJObPn1/Y7tG7d2+k/F97dx4XVfX+Afxz7iwwCyggsiiIuCMKLrlmuaSpKZrllktaZpamlVr2dV8yc0/NJbVcUjF3c0PDXHMJcwlxA0QEBNlh9pl7z+8PlJ8LmuDAnRnO+/WaVzb3zr3PRZl55txznieloMjBgAED0KBBAzRo0AAZGRmPzWdOSUlBVFQUevToAQAYO3YsWrRogfXr1z+zQhTDPIk4+qKplxUUFBRbVCe9adOmQa1WF67CvXfvHr744gucO3cOnp6eUKlUGDFiROHChoSEBHTr1g3R0dFFnodSirFjx+LQoUOQSCSYOHFiYd3GR2s89u/fH5cvF9wVmTJlymO1HS9evIhly5Zh7dqCgYnFixdj9erV8PPzw549ex67NRYUFBQXExNT82V/PsVFCKlIZCSl1pxaCrlH2Yy2WpMlz4LE+Yk8yYNk6KJ+CG5bV+yQkJGUhe97LtPpcnVfW8z8MrHjYRyDRCIZrFKqV66as0mRnJaEid+NwlsN69LWNQMcZwJyMRy/EYdjN+8IapWyxO2rc/I16PXVZIPJbK5OKU0tpVCf61mfaeWVWJ+FYrh8+XJCSEhIhthx2JrLly9XCgkJCXjyeZYg/wdHfTMR802Bc+KWuL3mNtx3oK9d3a/NjMxE5o5MGty2jjBg9jsSVQXbWeiRnpiJee/8qNPlG6abDea5YsfD2DeJRDJUqVD9uHLOr4rqfgVvExf+PYfx04fTTkG16Ot1A8vl3UdBEHA45hbOxCeVqH312j37LVuPHN2q1RsGlmKYz+Won2klxRJk5lkJcrl8k2PERU10bvaJbGrRvPT6lDJh0Vhwe8ZtPmdPNj5Y1J8M/3GQTSXHAODp74EJe0YrXdzVU52U8tmkuENbDIOCUm5OcqepFVwqLls1Z1NhcgwATRo0x9JvN5DD12LJ4as3rTMh1868TPtqo8mErUf+NOsMxm/LIFSGYV4SS5CZMkcpTSIc2Zt5JNPmM+SsE1m4/XU8rVHdHzP//BqhneqLHdIzuftWxDd7RysrelcYLVfKl7IkmSkOQohE4axc4+XpM/6XRTuUAX5PDzLWrxOCld+HkxO37nD7L18TyusdyMfaV/MF7au/WLjsue2rd/55UiAEJyml18owVIaxOXFxcbIOHTrUqFatWrCfn1/w0KFD/QwGA/nrr78UW7durfBwvy+//NJ3ypQpXmLFycq8MaIQDMKkjEMZYR4dPaRSte39M7ToLEhafJe3pFgkg+b0Jk27hZRKXWNrc62kxte7RqkW9V81JP1OZiVCyPuUUqPYcTG2jRCiUCqUu2pUq91m/uSVSrXK5Zn71g6si7ULtpOPxvWGief5no3qS8rrdzG5VIr3WoQSncmEreevcMO/nY8WDYL4T97pKanq5Vm4n85gxLp9B41aveHp1nFlTKPRJAYFBYkdhs3QaDSJYscgFl8fn5B7qalW+wD28fa2pNy799zScYIgoGfPnjWHDRt2f8yYMXEWiwXvvfdetTFjxlSpX7++PioqStW3b99ca8RjsVgglZb88tgc5P/g7+9/VK1WW7cdmg3QaDSJiYmJ7cWMQaKQrHN73a2/T38fm1qtl3M2B+m/3qeBjaoJQxf0k7h6PjtZsFUGrRFrR2/W3ToXH2/QGjtTSp89tMWUa4QQD6VC9UfzRq3rTP3ie4VM9mK/jslpdzF0zNtCkI8H7d20oYQrp0nyo3J1BoT/fVm4k5XLvfFKY/6jt7tLPN0qYsP+CH7ToSMHNDp9mNgxMuXXk3OQCSFNTq2x3rruV4eNAqX06eLZj9izZ4/LzJkzfaOiom48fC4rK4sLCAhoKJVKKaUUXl5e5rFjx967du2a4u7du/I7d+44paSkyEeMGJE2adKk+wCwfPly9xUrVniZzWbSuHFj7YYNG+5IpVIolcpGw4cPTzt69KjrvHnzkvbs2VMhIiKiokQioW3bts376aefkor4uRQ5B9n2hu5sjNhJpCMTDMLErKNZfSt1rgSZm/jNBwSDgMQfEnlzoknSb3pPtOjVxG5HxpxVTvh0zRDlwWVH6x5acfRfQkgYpfSU2HExtoUQEqBwVhzv3vEd71FDxsuLaCb0TFW8/PDrsn3c4NFhwuazl4T3modyjtRhryQetq9Oz9cg/PwV9P3fdHR7tSV/6Mw5s85g/Ers+BhGbP/++68iJCTksVKv7u7uQpUqVUwDBgzIuHnzpvOGDRsSAeDLL79UxMbGOv/11183cnJyJPXq1QseP358+tWrV522b9/uHhUVdd3JyYkOHDjQf+XKlR6jRo3K1Ov1XHBwsH7x4sUpaWlpko8//jggPj4+muM4ZGRkFOtOMJuDzIiGUpoMgpVpO9NE7xySeyEXcePjqI+LJ5n2x3i0fKep3U/hJYSg62cdpMOXD3JzUjkdlsoko+z+ohirIYR0cpI7Xxk+YIzv6A++LlZy/FDlSt7YvPwgdztbg/V/XeBftkawo/B0UeOzDq0kn7ZtjshzfxMCRFFKr4sdF8OI7UEb86emLjyrvXmnTp1yFAoF9fHxsbi7u5uTkpKkhw4dcomOjlaGhITUq1u3btCpU6dc4+PjnYCC7o5DhgzJBgB3d3feyclJ6NevX7X169dXVKvVxXqDYgkyIypqorNyz+byxlRxpskKFgF3Ft4R0tam4d1vuuHL8BGcm3eF/36hHQluWxcT941RuPlW/N5JKf+VEOIsdkyMeAghnJPcaapa5bJ7wZSVLn26D3qpO4nuFT2wdWUEd09rImtPRgkWvlwWuChSBYUzdEaTUWswilbWjWFsSYMGDfSXLl1SPfpcVlYWl5qaKpdIJE8lzk5OToXPPWhtTiilpHfv3pnXr1+PuX79ekxCQkL0woULUwBALpcLD+cdy2QyXLp06do777yTs3v37opt27YtVkcvliAzoqKUZoJibtq2tDLvrpd/JR/xX8YJlSRumBoxFm36N3fYAdbKAZUw6cAXyjqtar7tpHL6mxBSReyYmLL3YL7xYf8q1cf/umSPolHwK1Y5rovaFVtXRHA5JoqfTpwXTFZqy2zvIqJvGgjwM6X06b7LDFMOhYWF5RsMBm7ZsmUeQMFCuk8//dSvd+/eGd7e3maNRvOfeWnnzp3z9u3b55acnCwFgLS0NMnNmzefWjyRm5vLZWVlSfr27Zu7cuXKu9euXStWfVaWIDOioxa6MP/ffLP+jr5MzidYBCQuSRTurUhB2BdvkvHbP+U8qrqVybnF5Kxywic/va94c0TbunKF7F9CyKtix8SUHUJIa2cnxfW3Orz96pp54SpPD+tWT1IqVdiyKoLTw4muPHZWMJptvopjqcrI1+LCnSTexPPTxI6FYWwFx3HYvXt37M6dO92qVasWXL169WAnJydhyZIlyV26dMm/efOmom7dukGrV69+5odykyZNDJMmTUru0KFD7dq1awe1b9++9t27d59ayJSTkyPp3Llzrdq1awe1adOmzqxZs+4WJ1ZWxYKxCURCRqtqq2ZXn1Bd9d97l1z+tXykrUwVPKt64KNlA7nKAZVK83Q26+rxG1g96lc9bxEWmQ3m6ZRSk9gxMaWDECKVyeQTZFLZ/6Z+8b3i1WbtSvV8ZrMZg0eH8RZ9DvmkbQtOIRd/Aa4Y1p2O0l2/lz7HzPMzxY6FYYCnq1iIUebNFrFW04xNI4Q4cU7cbb+Rfj4uDa1fVk3gBST/lEx1l3Wk+xcdhTc+fI3jJOX7BkpOWi7Wjd2qu33pbopRa3yXUmp3b2zM8xFCGioVqvBA/5r+M8YtUHl5+pTJeXmex9AvevH5OancyHYticrJpio5lrrErBys+PNstpnn/SilWrHjYRiAtZp+FtZqmrFplFKjYBSGJf+crBXM1l0Jr43VIn5cvKDMdqYT941Bp+Fty31yDAAVvSpgzMaPlH2nhtVwUsrPyJ1lMwkh5XO4z8EQQpycnZxnK5yVZ0d/8HXdlXM2lVlyDBQsplm3eJekUuVqwg9/nKb5hvLTq0YQKLaev6w18/wXLDlmGPvFsgTGZlBKDwhG4a+MAxlWmbwoCAKS1iTRpPlJ6DikDSbt/5zzrlHZGod2GIQQtOr9Cpl6ZKzCSaD/UxKSSAhpKXZcTMkRQloqnJU3Quu/MmbL8v2K7h3fEWXxKcdxWDU3XFItoJ7wwx+naI6ubNYYiO2vuAQhR2e4BmCD2LEwDFNyLEFmbIqgF4an7083mzJebkqsPkGP+PHxgjxZigm7RuGt0R05idQuukWL4vCqY7QiODLFy9vbleMiVRz3MyGkothxMS+OEKJSOCuXq5Uukd+MmlltwZSVSk93cb8QchyHZd9ukNSt24T+8MdpmqUt82I1ZSpXb8CBf28YjRbLYMrmLzK2RxAEwTFLNZXQg59HkbetWYLM2BRKaQIo5qVsSCnRJ6kgCEhen0wT5ySiXb9WmBoxllSpW3a3lu1RWlw6zv56lsz38SVhFSogIrCGorOra39nQm4TQt4jhLD3CRtGCOEIIQOdnRR3WjV9bchvKw8pOrzaucii+2JZMOUnrkloG/rDH6eRnu+4sw52XojWUUqXUkqviR0LwxQhOj09vQJLkgsIgkDS09MrAIguajtbpMfYHEKIM+fExRd3wZ4h2YDkxUm80knBDV8+iFRrULUUo3Qc01p9y7fXE0zy8n5siP2SXo/Jqfc0qRZLmlYQvgCwj42K2RZCyKtKhWqVl6eP//gRU9QhQU3EDum5Zi3+hh47fZCMbN8S3hWsvxhXTNfv3ceGM/+kmix8IKW0fMwnYezKhQsXKkul0jUAgsEGSIGCkeNoi8UyrEmTJvef3MgSZMYmEUK6SitKf6s9r7aKk/337/G9LfeQdzwPrw9oKfQY35mTOVmtco1D27/kCP5acpRGBNYgyiJaDVNKcVSjwdz0+5psnk/UCMLnAP5gibK4CCHBTjLlDyaLof24EZMR1vFdlKRVtBgWrJxBD0TuIJ+0bYEqbo7RtdJs4TH7wJ+6fIPxHUrpIbHjYRjm5dnHOypT7rzogj1jqhFxX8fx/GUL/WLzcLw7qRtLjl9QTlou/vghEt/7+BaZHAMFi/g6uLjgYPVA9XQv7yBvqXSXmuOiCCFtyjhcBgAhpLpCrtrmLFOe79pkUFvPCt58Wvo9wV6SYwAYO2IK6dllAF3+51ncycwWOxyrOBJzy2yy8EdZcswwjoONIDM2ixASQOTkas0ZNZVO3k5PbU/dkYq8P3LR6t2mfK9v3pLIFeWr1urLmv3GfL7BfT3m+VZ54dWLFkqxLy+PLky/rzNQekkjCF9SSs+XZpwMQAhp6ixXTRYEvlP7hu9KO4T0kSrkKsTdi8byQ1/jt5WHUMndU+wwi2XtlmX4dcdP+LDNK6jh6SF2OCV2P0+DRUdOas28UJtSmiJ2PAzDWAdLkBmbRqTkc+eqzrNqTK2hIlzBugJTuglJi+7yUrOU+2jpAFKreaDIUdqf47+ewf6pexARWAMVJMWv7mGiFLtyc+gP6Rl6C+hZjSDMAnCMTb2wHkKIBEA3hVw1VcJJ63QM7efcqt5bnEL+eLPJebs/5evXr4eJo7+1uzItm3b9jDW/LsaQV5uitpf9dbWklGJp5F/apOzcibwg/CB2PAzDWA9LkBmbRgjhOGcuqvLblRtWerOS5P7v95FzIBtNu4fyfaaESZxVT48sM8+ny9NhcpOZ9NvKXqSTi+tLHcsoCNiRm0t/zsrU5gpCll4Q5gnAekppvpXCLXcIISpCuCFyqdNEd3Vll86NB6lDA1+DhCs6/03PTcacnR/hl4XbUa2q/X1Z3HFgM5aunYNBLRshyNdL7HCKJSohie765+oNo8USTCnlxY6HYRjrYQkyY/MIIbUgw2VlZYWc03OSD394D/VerSV2WHZrQa9lgtfNTPpTVT+rjThSSnFer8O6rCztWZ2OkwBbdJSuAHCBjSq/GEJIsEziNAygH9b0DSGdGw1QBXoHv1C5tpUHJwpKd44umrba7kaRAeDA0d2Y++MU9HulIQ3x97WLElR5egPmHjyuN1gsr1FKo8SOh2EY62KrmRibRym9RSRkOZ8jfDnz9NdQVVCKHZLdurD/MpIvJ3Frqlt3pJEQguZKFZorVao0sxnbc3MHb83J7mugNF1KyDIe2EgpfaqMTnlHCPEihPR3lik/VTq5VGlVt6usdb23ZJ4VqhTrOIPbfcNNCe+HyzEXYOul3orStX1POMmdMXPRV8QsCLRpQFWbTpIppdh87pKOp8JSlhwzjGNiI8iMXSCEcM5qp3NdRrYPfXNEO/bFrgRMBhMmhk4Xxld0J+9WdCv1BESgFBf0eoTnZOuOajQSZ0LO5wpCOICDlNLbpX1+W0UIcQfQS+mk/shsMYUEV2vBt6r3lrKObyNwz5hG8SK2nFiEe7obwobFuzhbahJSHKfOH8XkuZ+je0g92rJGNZu9iNOxCcKBK9dvGC18CKXULHY8DMNYH0uQGbtBCAmQO8uiv941SsW64xXf8qFrBXL2Djb7VyvzBErD8zip1SJSk687odUSAJkWSncaKN0D4BSl9OV6i9uwB4vtGhHCdVLIVb1MFkNw3SpNLM3rvKmq798ccql15tFbBAsmbnqXfjNqBmnXqpNVjimGqMtnMH7mCHQOriW8VjvQ5urXFVStOKUz83xjSukNseNhGKZ0sASZsSuchPvQs5rHD5MPfqli9Y5f3PXTt7By0BrsCagOP7m45fAEShFjNOC4RstH5OdpE81muYKQU7mCsBUFo8vJogZoBYSQ6gA6KuTqt828qY2LoqIQXK2FU5BfM3lt31A4yRSlct6Ii5txJvZ3uuOnw0QqlZXKOcrClWsX8fmUobRdner0jaBaNpMkW3gBCw+f1GZqtF9ZBGG52PEwDFN6WILM2BVCCHFWOe1v1qNR+/e+7cVKWLwAQRDwv4ZThQ8VrvjQw8Nmko2HsiwWnNJq8YcmX3taq5VyhGRKgPN5gnAcQBSAS5RSjdhxPgshRAkgBEATZ7mqDaXC64RwrnWrNBGCq7VQ1anSGG7qsqtRPGlTH35o/xHcu2+9Z7NTFF7E9dhojPxmEG1V0492Ca5jE9NG9l2+ZjoTl3jaaLF0YItPGcaxsQSZsTuEkIpyhfz6+/P7eDXp2lDscGzeL19uoRn7r9JdAdU5qQ0kGc9joRRxRiOuGg24rNcb/tHrjbdNJpWLs3OeRCL5O1evP8cLwr8AogHcppQayyo2QogcQACAYAkhwRUUiuYGC9/MYLG4ebh4a6t71ZdV9wpSBHoFwdc98IWqT5SGqNij2HbmB+xaexQqheq/X2DDbifG4qNxfWjTaj5CWGiQRMwk+UZqOtadvpBt5vm6AD4AsJ9S+q9oATEMU6pYgszYJUJIUyel/MSkA18oPKvZbxeu0pYYnYSFYUuxxb8a6jg7ix1OifS5myi83uwVrnWNQPybnMJH3bmj/Tc5hUvPz1dKJRKjs1SaLZVI7lPgntFsvqM1mRIBpAFIBXAfgB6A5ZEHD4BDQRWfhw9nAF4PHt5KudzPWSatRkB8LYLgaTCb3S087+yhVuuCfX2FptX8lQ2rVJGuOnkKgqyO0P+1L21qZH7mb+/zb3boQj4e+LlNxVUSyfcSMfSLt4VgH0/6TtMGEk6EJDlPb8C8Q8f1erOlG6X0KCHkPQBLAEwD8CMbTWYYx8MSZMZuSWWSMZ7VPL6duP9zlczJfudblhZBEDDllVn825CTLzwr222i1P5uIv/zB0MlXYLrP/a8IAjI1umQlpeP1Lw8pOXnITU3Dym5uXxSdrbhbnaOJS0vDybeQiy8QHih4GGhApEQjkq4goeU46hcKqVeLi6o6lZR4u/u7uxToYLU29UV3q6u8HJ1gberK9yUSnDc4z/GeYePYMnxK8KEd3+yqZ9vfGo0fjxony2oi5KWfg+DR4cJtSu70X6vhEg4ruySZEGgWH7sjC45O3eJycJ/8/B5QkhtAJsBpAD4gFKaUWZBvQR/f/+jarXaX+w4Skqj0SQmJia2FzsOxvGxVU6M3eItwpKctLxOv03f237A7Hfsc3i0FO2YvR+yPAM3srp9NF4oiiAIyDGZJKF+VZ/axnEcPNRqeKjVCPJ9rKqJBECZzC0IC2mIiXv2c5RS0aZUFCXQOxjeFQP4lRsXYdKY2XbZPORRXp4+2Lz8ADdwVDdh45l/+IEtG0kkXNl8J4m8Fmu5l5N3zcwLkx99nlJ6kxDSCsAsAJcIIUMopX+USVAvQa1W+8fExNQQO46SCgoKEjsEppywqVEPhikOSik1aIzvnd9zMTtq32V2K+QR6XcycPqX05jv60vkZZRIlIYYoxEyiQTeri/XEru01PHyAkc4ZOanih3KU4Z2mCQ5evqQJOFunNihWIWHmye2rozgkvIN5JdTUYKFF0r9nLH3M3H0eqzOaOF7UkotT26nlJoopV8BGAJgPSFk7oO56gzD2Dn7/eRkGACU0lyjztR949fbDGm308UOx2YsH7CaD3Nx5Rsp7Lvr4DGtBkE+Prwtjc4+qYJSxSdn2l4SWsnVB7V8GgmL13zHix2Ltbi6VET4ygguwyjQ1SfOC2ZL6V1aRr4W605F6c280ItSmvS8fR+MHIcCqAvgrwfTLxiGsWMsQWbsHqX0gsXMj10yeI1Wn6cXOxzRHVp+FMbUPMn4ypXt/tb6Rb2etgwMtN3sGEANjwrc3YxbNnkHY3Dbb7jo65cll65eEDsUq1Er1di64pBES6V05fGzgtHy1MDuS9ObzFh1/JzOzPPjKaWRL/IaSmk6gB4AfgFwmhAylNjyNzsAbdu2RURExGPPLV68GJ9++il+/PFHhIaGFj6Cg4NBCMG1a9eKPFZeXh6qVKmCUaNGFbl93bp18PT0LDzemjVrAAA3btxAkyZNEBISgjNnzgAALBYL3njjDeh0OiteLcMUD0uQGYdgMVlWaLK0m5cPX6/jS3FUydblpuchYuFh+p2PD1R2PLXioWSAb1rN36YvpHn1AHI7Lab07/eXgNJZjSaB7bFg1QzBkRZkOzsrsWX5IQkvU9PlR88IBrP1uj3zgoBfTkfptCbTJosg/Fic19ICPwJoB2AsgC2EkIpWC87K+vfvj/Dw8MeeCw8PR//+/TFy5EhcunSp8BEWFoYBAwagXr16RR5r8uTJeP311597vr59+xYeb9iwYQCAVatWYc6cOdi+fTvmz58PAFixYgUGDRoEpdK+74Ax9s2mP3gYpjiMOtOnd68mX/xtxu9lVhvX1iwfuIZ/TakS2qjUYodiFTkCX+QCPVvSuX4QkjPjbPa99N1XP8P9jDRy7MxhsUOxKrlcjk3L9kvkLpXo0si/BJ3ROt3K91yKMSZn5100WfhPS3oMSmk0gFcAZKBgAV9rqwRnZe+++y727dsHo7HgLTMhIQEpKSl49dVXH9vvxIkT+O2337B8edHNAy9cuIC0tDR06lT8FucymQx6vR46nQ4ymQw5OTn4/fffMXjw4OJfEMNYEatiwTgMSqmFENL97I6oy1Xqevu+9l4Lu59iUBynws8hM/a+ZGqg3S5Qf0y6xQK92ULqeHmJHcpztatTG3qTluiMGiidbO+LiZSTon1wX7J49Xe0TbP2dt2C+klSqRQbftgjGTauN/9D5Gk6qn0r4uJc8gabZ+Lu8FEJSfdNFr57UYvyioNSqgcwihASAWAHIWQXCupyi8EAYOGTjXU8PDzQrFkzHDp0CD169EB4eDj69u37WEWWnJwcDB06FBs2bIBrEYtlBUHA2LFjsXHjRkRGPn82yo4dO3DixAnUrl0bixYtgp+fH0aOHInBgwfDaDRi1apVmDFjBiZOnGhTVWGY8slmRz0YpiQopdlGnanD9pm/626ciRU7nDKjzzdg15TddLqXNypKHON7wZ8aDQI83AWpjV+PXCqF2lklpGTGix3KM3Vq1B8WiyDsOvSb48yzeIDjOKyZv01SpWptuuSP0zRXbyjRcWLvZ2DvpRidycJ3oJRmWys+SunvABoDSAIgiPgo0qPTLB5Or3jUJ598goEDB6J166IHwZcvX46uXbvCz8/vuT+H7t27IyEhAVeuXMEbb7yB999/HwDg7++PY8eO4cyZM1AqlUhJSUHdunUxaNAg9O3bFzdv3nzucRmmtLBGIYxDIoS0d1Y77fvf72MUlQMqiR1OqVvUeznvFnMfa6v6idqO15rGpSSjQt06/OYPP7DtDBlAvWnf8sGBPSVtG/QSO5RnuhB3DL/9tQi71kRCpbS9kW5r+Hzqh8KNGxfJqA6tiLvqxeevZuRrsejIKb3RUtAprxRDFF1QUFDso3WQNRoNAgMDcejQIfTv3x83btwo3Hf9+vVYuXIlTp48Cam06BvOAwYMwMmTJ8FxHDQaDUwmEz799FPMmTPnmTHwPA93d3fk5uY+9nzfvn0xa9YsrFu3Dm3atEFAQABmzpyJTZs2PRp/XExMTM0S/wAY5gWxEWTGIVFKj5oNlnGLB/6k0+Y69kroixHRSPznjmS2t4/DJMcAEMvzfMvA6jafHANAIz8fScL9aza9OrRJjbZQO1XkN2xfbZMLCq1h8fS1XEjDVnTJH6eRodG+0GseVqyw8Pw4R0+Oi6JWq9G2bVt88MEHj40ex8fHY+LEidi0adMzk2MA2LRpExITE5GQkID58+dj8ODBRSbH9+7dK/zz3r17n1rsd/z4cVSpUgW1atWCTqcDx3GQSCSskgUjGpYgMw7LYrYs12br1i4e8JPWqLPOAh5bYzFZsOXzLcL4SpWpt8xx5pYCQBalJLTq82/b2or2dergzv0bNv/tZMBrX0m27fuVS88Sayps6Zvzv2Vcy+YdhSV/nEZaXv5z932iYkXRK9DKgf79++Py5cvo169f4XPff/89tFotevXq9Vi5t5MnT77wcadMmYK9e/cCAJYsWYL69esjJCQES5Yswbp16wr3o5Ri1qxZmDy5oFnh8OHDMWHCBLzzzjsYN26cdS6SYYqJTbFgHBohhHNSyjdXa1i1++j1w5RSuWOtS1350S+C5dRthPtX4zgHGj02CQJeiY9D+vy5qKBQiB3Of7qflwffCROx8IMDkEhs+9/Y/N0j+br1amPy59/Zxeh8SX3/41R65Nhu8km7lvCtWMTiMkoRfv6y4Wpy2t9Gi6X9yy7KsxdPTrGwN2yKBVNW2Agy49AopYJRZxqY+G/y6bWjN+sFwXHuLt88F4/rkde5uT6+DpUcA8A5nRZuSgW1h+QYACq7usJJ6kzTcu+KHcp/GtJ+kuTPvw5Lbt917EWsX4+cTsI696c/Hj2Du1k5j22jlGL3xavGq8mpN4wWS5fykhwzDPPiWILMODxKqcWgNfa4dupW9JbJu4yOcNdEEAT8PGwdP8LDQwiQy8UOx+pOanVoWKWKXX2bcVep+KQM2086K7n6oLZPI2HxasdpQf0soz+cQPq+/QFWHDuL2xlZhc9HXL1pjkpISjRa+LaU0hebrMwwTLnCEmSmXKCU6g1aY8fzey4m7F0QYb22WyL5dcJ26mYSuA/cPRzyd/iq0UBb1ahhV9dW19tdcjfjll0k9YPbT+Cu3rgiuXQ1SuxQSt3HA8ZgaL/PsPr4edxKy8DxG/H8iZu300wWvg2lNEfs+BiGsU22PVmOYayIUppLCHkt8pdT/6jdVd4dPmhjl3Mwk67dwz/bL5BN/tUgdbCpFQ+lESI09vOzq7+fVwNrkHV/X7WLBFkhV6NpjQ6Yv3KmsHHJbs6Rqp8UZfC7H8HZyRnL180FQPLMPN+aUpomdlxi0Gg0iUFBQWKHUWIajSZR7BiY8oElyEy5Qim9TwhpvWf+oSgnpZP7q/2a2dUoJQCsGrya71/RjdRzdra72F9UrsUiCalq2y2mn/RWg2B8d/iohFJqF13A3mk9CpM2vUv+/Osw2rd+U+xwSp1KqRYIJ80zmU2dKKXlNslKTExsL3YMDGMPHPYDlmGehVJ6x6Q3t/ptxp7sU+Hn7GLE76Gdcw6AyzFwoytVctjf3TiDAQKlqObhLnYoxdLY3w+CwCNXlyl2KC9EyknRoUE/snjNd9RsdswyiA8d/HMvXfjTrFyT2dSSUur480oYhnlpDvshyzDPQym9ZdKbW/w2Y2/2yc1n7WKxUmZSFk6sPoF5Pr7EiXPcX92jWi3qeHkJ9jAK+yiO4+CqUPHJmXFih/LCOob2A28RhN0RjteC+qHDx/fR+Sun5xqMhlcppdfFjodhGPvguJ+yDPMfKKWxJr25xbZZv2cf2/iXzY8k/zhgNd/VxYVvqnzxFrr26B+9Di0Cq9tlwlbN3ZUkZcTaVexvN/9UsnrzUqLVacQOxeqOnDxA5yyfmmcwGtpQSmPEjodhGPvBEmSm3CKEOAOoZdKbm+/87kDGkTUnbHYk+cjq49Al50gmeFa2q4VrJXGHUr5ZQIBdXucr1fy422kxNvvvqChNarSF2tmNX7/tJ5v/klgch47tpd8tnZxvLEiOo8WOh2EY+8ISZKZcIoQ4AdgF4B1KabxJb2q6b9Hh1ANL/7C5EnCaLA0Ofn+QfuftA7XELvPGYskVBC6kahWxwyiRjvXqISnzlt39JQ187SvJ9v2bHKIFNaUUG3esscxbMT3TaDK0opT+K3ZMDMPYH5YgM+UOIUQOYBsALYARAEApvWvUmV6JWHksecfsfSZbaiby48A1fCuFUnhdrRY7lFKXZ7Eg32QiQT4+YodSIl2C6yNPl0OMZr3YoRRLda8g+LhV51esX2hXo99P4nkeC1bNNK7ftirRYDQ0opReFTsmhmHsE0uQmXKFECIDEA5AAND/0RazlNJ7Rp3plZObz11f89kmvcUkfvfZM9ujkH4jVTLNy9vuRiVL4rhWiyoVK1JnmUzsUEpEKZdD5aQU7mUliB1KsQ1tP0ly7MwRSXyi7XcDLIrRZMT/5ozWRxzfd1lv0DWhlCaJHRPDMPaL1UF+Qf7+/kfVarW/2HFYi0ajSSxv9TAJIVIAmwDIUTC14qnpFJTSDEJIy6vHbuxcNOCnV0f9/IFK4eJc5rECgFFrxI7/7aSTKnvBXSq1r5IOJXRGp0XjwEABgN1+IajsqqZJmbEI8KondijF4uHqjdq+jemi1bOFpTN/tquff54mF19O/1h7Jyn+qE6v7U0pNYodE8Mw9o0lyC9IrVb7x8TE1BA7Dmux505KJUEIkQBYD8AVQM/nfYBSSnWEkLeSYlJWfRe2pN8XWz5WuXlXKLNYH1o57BehnlSO7q6u5eZOzw2Lhf+wRqBdJWdPauhbWZJw/zr/alB3u7uOwe2+JlO29OcuRv+NRsGviB3OC0lLv4dRk4bosnIy1xuM+s8opQ612JBhGHGUmw9epvwihHAAfgbgBeBtSqnhv15DKeWNOtNH2fdyvvv2rcW6lJuppR7no65ExiDh3G3uOx8fh28D/KhMAPbWQe9Jr9eqhTv3r9vlX5pCrkbTwI5k/qqZgi3Nw3+W+Du3MHRsb11G1v3peoNuJEuOGYaxFpYgMw7tQXL8E4BqAMIopS+8eopSSk0G87e6XN2Iub1+1N88WzYNICwWC379bLPwhacn9bXTubglIQgCck0mib1WsHgorGFDpOcmc4Kd5mrvtP4UGZnp5OjpCLFDea6L0X/j4wkDdHn5OcOMJuNcseNhGMaxsAT5JajVaiQkJEChUCA0NBRBQUEYMWIEBEFAQkICCCGYPHly4f4ZGRmQyWQYNWpUkcc7duwYQkNDUb9+fbz++uvPPfdnn30G9SNVDXbs2IH69eujTZs2yMwsaHUbFxeHfv36WeFK7RMpGHr9EUBdAN0opbqSHIe3CBsNWmP3ZR/8rDmzParUh9V+GbWZ+goE71V0s8tRyJL6x6CHQiaDp4uL2KG8lOqelSCVSJGRlyJ2KCUi5aR4o2F/8sNa221BffT0ITpu5giNTq/tLgjCFrHjYRjG8bAE2Qpq1KiBS5cu4cqVK4iJicHu3bsBAIGBgdi3b1/hftu2bUP9+vWLPEZOTg4+/fRT7N27F1evXsW2bdueeb6oqCjk5OQ89tyCBQtw9uxZDB48GJs3bwYATJo0CTNnzny5i7NTD5LjJQBCAXSllL5UmzBKaaRJb24ePnV3yuZJO428uXSqYcVdSMDVw1fJfB9fjitHUysA4IRGi+AqvnZdZuyhikqVxZ5aTj/pjZA+ECwQdh3aalPzLCil2LB9teXbJROzH7SOPip2TAzDOCaWIFuRVCpFq1atEBtbUCZJoVCgXr16iIqKAgBs3boVffr0KfK1mzdvRq9eveDvX1Aoo3LlykXux/M8xo8fj7lzH7+jyHEcjEYjdDodZDIZTp48CR8fH9SqVctal2c3HiTHCwC0ANCZUppnjeNSSmOMOlPw+T0X/5rXe7k2Lz3fGoctJAgC1n7wCz/M3UMIdHKy6rHtwWWjgbauUcMh3pNqeVaUJKbfsqnksrh6PWhBrdFa9995SWl1Goyf9alu447V1w1GQyil9LLYMTEM47hYFQsr0ul0iIyMxIwZMwqf69evH8LDw+Ht7Q2JRAJfX1+kpDx96/XmzZswm81o27Yt8vPzMWbMGAwePPip/ZYtW4awsDD4PNFIYerUqXjzzTfh6+uLX3/9FX369EF4ePhz432QSDYBoCrRBduuHgDaAuhAKc215oEppTmEkDdSbqR+O6PzwtGjfv5AGRDiZ5Vjb5m8i6r1Fu4jb4/yNXT8wD2Ab+zn5xDvSS0DA8jOf6/ysONydY1qvI79//zCb9j+E/n0/bGifnG5fTcWX07/WJevyftNb9CNYGXcGIYpbQ7xYSS2uLg4hIaGghCCHj16oEuXLkhISAAAdO7cGZMnT4aXlxf69u37zGNYLBZcuHABkZGR0Ov1aNmyJVq0aIHatWsX7pOSkoJt27bh2LFjT72+Y8eO6NixIwBg/fr16Nq1K27cuIH58+fDzc0NP/zwA5RK5ZMvkwGYAcdLkDMAdKSUZpfGwR+slP+GEHJ+Yb+VG/vN6Kls1fuVl0pqU26m4e/w82SDnz9k5WxqxUO5PC8J9bPvChYPdakfjGXH19ptcvzQwNe+lizdPw69uw2Ep4eXKDFEnjpEZy+dpDeZjZ/xPP+zKEEwDFPusATZCh7OQS6KXC5HkyZNsGDBAly9ehW///57kftVrVoVlSpVgkqlgkqlwmuvvYbLly8/liBfvHgRsbGxqFmzJoCCEeuaNWsWTul4+Nz69esRERGBTp06Yc+ePdi8eTM2bdqEjz766LFzUkpNALq+3NWXX5TSXYSQZlun7YlIuHTXs8/UMCepvGS/UisHreZ7u1ZEsLPC7pOqkkg2mWC0WEgNT0+xQ7GKV2sEwmjWQ2PIhdq57GtoW0uAVz34ugfyyzcsxNQvvi/Tf5tmswnL1s037ftjZ47BqO9CKf2nLM/PMEz55hDz/Wzd2LFj8f3338PDw+OZ+/To0QMnT56ExWKBTqfDuXPnUK/e45243nrrLaSmpiIhIQEJCQlQKpWPJccAMHfuXIwZMwYymQx6vR6EEHAcB52uRAUcmP9QOC9578UT3761WJsWn17sY+xdEAGapeM+9/Qsl8kxAPyp0aCmp6cg4RzjLUkqlcLFWS0kZ8aLHcpLG9JuouT4mSOS+Du3yuycKWlJ+HBcH+2Bo7tPGYz6+gAuldnJGYZhwBLkErNYLHB6wYVU9evXx/vvv//cferVq4fOnTujYcOGaNasGYYNG4bg4GAAQNeuXYuct/yklJQUREVFoUePHgAKEvMWLVpg/fr1eO+9914oVqb4KKW5Bo3xzfsJmV9/2+0H3enfztMXbbKQlZyNP5f/ibk+PkThIMlhSZzX69CseoBdL2p7UtWKBS2n7Z2Hqzfq+Dali1bPLpMKI8fOHMHgMW/rE5PvTNfptW8AeA3A/gfdMBmGYcoEsYduSbYgKCgo9tFW05cvX8ZHH32E8+fPixlWiQUFBcXFxMTUFDsOR0MICXZSyvfUbV3L+/35fZRKV8Vz95/Vfi7fONOEOT6+5frDv/vdRH5czzDJiNdeEzsUq3n/l/X4N93J8mHHqXY/lU1v0mDKlv507sQfSeMGzUrlHEaTEUvWzjEeOvZ7jsGoD6OUngcAQogUwB8A/qSUTi+VkzMMwzyh/A5ZvYSVK1eif//+mDVrltihMDaGUhpt1JmCr5++9evU9vN0sX/ffua+R385ifw7WZL/VfYq18kxAORQSkKrWqcaiK3oUK8OEtNvOsR7rEKuxis1OpH5q2YKgmD9DoHxd25h6BfvaA+f2H/UYNTXfZgcAwCl1AKgH4DhhJBOVj85wzBMERzizbusjRgxAjExMejUib1XM0+jlOoNWuPH+ZmavksGr8nbs+CQmbc8fndak6PDvtkH6CxvH7hKynd+rBME5BqNXIMqvmKHYlXdgoORrU3nLLxZ7FCsolerT5CZlYGjf1mvBbXZbMKazcvMw8b30yalJo7V6bVvUUpzntyPUpoKYACA9YQQxyh1wjCMTWMJMsOUEkrpPpPBXPfPdafPz+q6WJt8I7Vw2/LBq/lXnBRCBztvq2wNf2m1qOziQlUO1hzFXa2GQqagqdl3xA7FKqScFB0b9ud+WDPHKi2oY25ewYDPwrRbf99w0mgy1LVYLKueN3mfUnoMBd0xtxJCZC8dAMMwzHPY/dy4sqLRaBKDgoLEDsNqNBpNotgxlAeU0nuEkDZpt9OHzem5dNGbH7eVe/i5ydKu3pOsqx4odng24bRWg1C/qjwc8P2okkrFJ2XGSqtWcozp/h1C+uDY1R3CzoPhXN+wwSUq2G0w6rHq1x9MeyK2GUxm4whKafgLr2oFvgfQGsAcAGNLcv7yzt/f/6harfYXO47i0Gg0iYmJie3FjoMpXxzuA6m0sF9OpqQefPivJoQc+mPtiY0Snfn1993caCWptHx2BHlCjMkk9AkMdMh5JkE+HpLE9Jt8izqdHeb6erUYKVmzZT7e6vA21Kri3QG58O85zFj0tU6n10UYTYaPKaXFqotIKRUIIYMBXCCEnKaU7ixWAAzUarX/owvO7YEjDU4x9oNNsWCYMkIpvWvQGNvpBGHMz1lZmm/T0ozaUljwZG/SCaGN/fwc8stCm5o1ye20GLHDsKpGga/BVeHBr9u26oX/8Wq0+Zi9dJLhq1kjszKy0vtpdZpexU2OH6KUZgHoA2AlIcQxhuYZhrE5LEFmmDJEKaUCpUsMlNbYnZf7+5vxcbrjGo3YYYlGEATkmM2SkKqOue6qe8MGSMtJlDhaOc2Br38t2XlgC3c/I/U/9z11/k/0HtFZd/R0xFaDUR9IKS26nWgxUEr/BjAdwHZCyPNrKTIMw5QAS5AZRgSU0nQNz/fO4vm3x6akJA+9m6i9bTKKHVaZu24yQspx8K1ov+2Ynye4ShUAQLbmvsiRWFdA5bqoUtCC+pnNQ7JyMvC/OWN00xaOv5eXn/OWTq8dQinNtWIYywFcQ8HCPaaECCEYO/b/p3PPnz8f06ZNAwBMmzYN8+fPf+Zrt2/fDkIIoqKiity+detWNGzYEPXr18dXX31V+PzSpUsRHByMrl27wmQqWPB56tQpfPnll1a4IoaxDpYgM4yIKKWHdVQI/Eenm94rIUE3Oy3NlM+XScMym/CnRoMgH2+eEIecYQEAqKBQ8cmZcWKHYXVD2k+SnDgbKYlLuPnY80aTERu2/2Tp/XFn/bmLp1frDfqaDypQWNWDuf3DAbQhhDy/VSnzTE5OTti5cycyMjKK9br8/HwsWbIEzZs3L3J7ZmYmxo8fj8jISFy9ehVpaWmIjIwEAKxZswZXrlxBo0aNEBERAUopZs6cicmTJ7/09TCMtbAEmWFERik1mSidZ6Q0cFde7m9vxMfpt+XkUN7BbssX5aJeT1tUr+642TGA6h6u5G5GrMP9Zbq7eKGub1O6aE1BC2pKKf44eQDvfPSG7tedayMNRn1DvUH3OaVUV1oxUErzAbwLYD4hpEFpnceRSaVSDB8+HIsWLSrW6yZPnoyvvvoKzs7ORW6Pj49H7dq14enpCQB44403sGPHjsLtZrMZOp0OMpkMGzduRNeuXeHm5lbyC2EYK2NVLBjGRlBK0wAMIoQsnpd+f8267Kxa0728VU2VSrFDKzVJAP9JQDWHfh9qFlCNi4y9ygNwmEoWDw1sN55M2dKf27p3Aw4c3a1JSUtK0um1H1NKT5RVDJTSaELIlwD2EkL+LKvz2hkDgLGUUn1RG0eOHImGDRs+Ng3ieS5evIi7d++iW7duz5yCUbNmTVy/fh0JCQmoWrUqdu/eXTidYty4cWjRogXq16+P1q1bo2fPnjh06FDJroxhSolDfzAxjD2ilF4ghDTWmEx9P066u6yxQuk81tNTVfcZIzX2LFfgHXaB3kNvBtXDhnNbHfJuXa42EwqZq2XNlmVGvUH3KaV0E6W0zEuzUEo3EkKyAVQu63PbCT2AZ7Z0dHV1xeDBg7FkyRIoFM9f8ygIAr744gusW7fuufu5ublhxYoV6Nu3LziOQ6tWrRAfHw8AGDRoEAYNGgQAmD59OkaPHo2DBw9iw4YN8PPzw4IFC8BxDvkrw9gRliAzjA16ML8ynBCy+5xO+8l7ibqprVQq2ZeVPJWBDtJxLtNigc5sIXW9vcUOpVR1rFcPWmMe0Zu0UMhVYodjFVn5afj975/1l+JP8JTSby2C+YdnjU6WFUrpPjHPb+8+//xzNG7cGEOHDn3ufvn5+YiOjkbbtm0BAKmpqQgLC8PevXvRtGnTx/bt3r07unfvDgD46aefIJE8fhMlJSUFf//9N6ZOnYpmzZrhzJkzmDhxIiIjI9GxY0frXRzDlAD7isYwNoxSajBTushAaZWTWu23795J0IxLSdbfNb18q1+x/anRoJq7uyCTONzMg8c4yWRQOamElKzbYofy0vL1Ofjt1FLjzK1D9JfiTy418yY/M2+aI3ZyzLw8d3d39OnTB2vXrn3ufhUqVEBGRgYSEhKQkJCAFi1aFJkcA8D9+wXVW7Kzs7F8+XIMGzbsse2TJ0/GzJkzAQB6vR6EEHAcB52u1KatM8wLYwkyw9gBSqnWJAizDZRWjdRoFoYl3NZNvHfPcM/8zLumNu+cToum1fwdbvFaUbxd1TQpI1bsMEosR5uB3Wd/Mk/dPEB/7mbEBjNvCjRZDF9TSnPEjo2xnrFjxxa7msWTQkNDC/88ZswYBAUFoXXr1pgwYQJq165duO3ixYsAgEaNGgEAPvzwQzRo0AD//PMPOnfu/FIxMIw1EEcrYM8w5QEhxMOZkP9R4JM31C4Y7uGhqGVnUy963k3kR3Z7SzK6fTuxQyl1fVavwZ08D35w+wl2NVyelBGLw5e26P5N+ItwRLLRaNHPoZTa/1B4ORYUFBRrh62m42JiYljXRKZMsTnIDGOHKKWZAMYSQr49nJ83KlKT/2VDZ4Xkk0oe6mYKJeyhrnAWpSSkahWxwygT7WrXxvSDp23/LwWAQAXEJJ7HoX82aVKy4sy8wM/jBctKSmm22LExDMOUFZYgM4wdo5RmAZhBCJn7t1438GqyYWplqbTiSI9K6k4uLpDaaKJsEgTkmkyco1eweKhHSEN8tnU7xws8JJxtDiKbLEacv3mERlzcpNUbNakGs246gN8opfY/4Z1hGKaYWILMMA6AUmoAsIYQ8vNtk+mt6Wmp02ffT6szzN3DuWeFClxFG1sId16vQ0WFglZUKm0zg7cy34oVIZfKaXpuEvF2qyZ2OI/J12fjWPQuy/F/d5kJIef0Ju10AMcpm3/HMEw5xhJkhnEgD2rQ/g7gd0JI8+WZGV//kJHepYPahQ5yc1M0dHa2iekXp7RaNKziK8ABm2c8i5tSJSRlxklsJUG+l52AIxfD9RfjjxOOk2w1mvXfUUpviB0XU7o0Gk1iUFCQ2GEUi0ajSRQ7Bqb8YQkywzgoSuk5AL0IIZUi8vM+OKbRfFFJKlENcnNXd3N1JRVEHFWONhrpWzVqlKsqOnW83LnE9JtC05rtRbtuvVGDi/HHceLq3ry03LuUCsJii2D+kVpoulgxMWUrMTGxvdgxMIw9YAkywzg4SmkGgLmEkAWJZqHtkoz0z+al3+/8ukrF963opmymVJb5XOVUQGjs71duRo8BoFVgdbL5n6tl3mVOEHhcT7qA09f2a6/ePS+VSeTH9CbNcgAHKaX2WyeQYRimFLEEmWHKAUJIHQA/U0pbA4gkhHhEajQDz+p0n1DAv4uLC9fdtYJTY4UCXBkky7kWi8O3mH7SWw2CsTDyZJl8KaCUIjHjJqJuRZrP3oiwALijN2mXATTcbDFmlkUMDMMw9owlyAxTPgwHcOLh/zwoE/cDgB8IITV25eb2P5if/6EEqNzdtYK0m6urvEEpzVeONxohUIoADw+rH9uWNQ8IgJk3IU+XBVelu9WPTylFSlY8omKPWs7dPGw0mvX5vGDZYOHN6ymlMVY/IcMwjANjCTLDODhCiBOAwQBaFLWdUhoHYBaAWYSQoN9yc97blZc71JmQCmGuFeTt1GpZqEJhtWkYf2o0qO1VWSCElKs5yBzHwdVZxSdnxkuslSALVEByZhyuJJzmz16P0GuNeQZK6a9m3vgrgH8AEBRMrxnPqlIwDMO8OJYgM4zjexvApQeJ8HM9GGmcRAiZrAVCN+Vk99mRm/OuiVK/lkqVuaOLi/pVlQqVpCV/67ig16FFvTrlMlnzd3PF3YxbtJ5f0xJ/28jKT8P15AuIvnNGcyP5opSAZAlU2GOyGDYCOPtoIvzgDkB3ANsAnHvpC2AYhiknWILMMI5vOIAVxXnBgyTr4oPHN4QQnz+1ms4X9Lo+BkpfryqTmTu5uKheV6kl9Z2dizW6fIdSflBAQLlaoPdQE/+qkr9TrvEoRnk7vVGDW/cu42rieWN04lmzzpBHpQUL7XYD+INS+swSWJRSSgjZBGAAWILMMAzzwliCzDAOjBBSG0B9AHte5jiU0nsAfgHwCyFEFm8ytfolK6vHpuzst42U+tZzctK3VqnUTRVKSUOFAkru2bMncigtNx30ntSxXl3svPL7c6eW8LwFCfev4VpSFH8l4bQ2Leeus5NMcVFv1OykoEcAXDZZjMWphrEZwGlCyFhWtYJhGObFsASZYRzbMADrrNku+EGSdfzB40tCiNtlg6H1daOx3a8k+02dINSuJpfrW6tUiqYKpSxUoSickpFvsSDfaCT1fX2sFY5d6RpcH7k/rycmixFyqRMAIEebgbvpN5GYflOIvXdFc/v+NWeZRH7Xwpv2mnnTAQCntYY8fUnPSSmNJYTcBvAGgIPWuRKGYRjHRti6jRfn7+9/VK1W+4sdhzVoNJpEVjDesT1YnJcI4FVK6a0yPK8CQDMJ8Lqa47roKW3oRAjqOTlbXCWc+iKl5K+vxpEanp6QPGek2dHwgoDbGRkI+XY+Dan+GjJyk/OTMmNlZt4syKVO/xrN+uO8YDkP4CSl1m3cQQj5DEBzSulAax6XYRjGUbEEuRiCgoJiY2JiaogdhzUEBQXFxcTE1BQ7Dqb0EEL6APiYUtpB5DgIgGoAGkmANi4KRSdeECobLBa3au7uulC/qpLG/v7KBr6+JNjXF/7ubuDsOHGmlCIxKwvRKSmITrlHLyQm6i7fTeJvZ2YqnKTSXIDc15stkbxgOQbgAoDE0q4wQQipDOAmgCqUUm1pnothGMYRsCkWDOO4hgP4SewgHiR/CQ8eux4+TwhRx6an14tNTw/e/290qFIub2Ywm+sYLRbXCgqFwdNFbfGtUBFVKlaU+bu7OXu7unLerq7wruAKL5eC/6qdnEqlVnMR1wCN0Yi0vDyk5uUhLS8fqQV/pnezsg1JOdmmlJxcpGvypTk6vbOTVJrvLJPd1JlM5/Vm8yUA0QBiTBZLfqkHW3T89wkhZwD0QMGcZIZhGOY5WILMMA6IEFIDQEMAu0UO5ZkopRoAfz94FCKEOGdqtV6ZWq339dQ0LwDeBPBWOztVk0uk/hTwsfB8Jb3Z7AaAc1MqDZVdXCw+FSpwfm4VZT4VKjg7SaVEKuEg5SSQchwe/lnCcZBwBIJAYREEWAQeFl547M9Gi4Wm5uUZkrKzzck5uTQ9P1+SrdM5U1DBWSbPkUsk6QBSzTx/V2M03hEKFjCmAUh98EgzG40lnjNcih5Ws2AJcjlm71MF2fRApqywBPklEEIwcOBAbNy4EQBgsVjg4+OD5s2bY9++fQCA3bt3Y8qUKTCZTJDJZJg5cyZ69uxZ5PF+++03TJs2DYQQhISEYPPmxz/HdDodevfujbi4OEgkEnTv3h1z5swBACxduhSrVq2Cv78/du/eDblcjlOnTmHnzp1YuHBh6f0QGFs1DMAGSqlR7ECKi1JqAHDnweO5CCHq+/n5Xvfz872jU1K8AHgDqEQIkUo5Ti4hRM5xnJwjRMYRIucIkQGQAbAIlJoppWaBUhNPqVkQBJNFEExCwSLEdPx/0psGIPVBQm/PdgNYRgjxtPYcZ8Z+qNVqf3ueKhgUFCR2CEw5wRLkl6BSqRAdHQ29Xg+FQoEjR46gSpUqhdsvX76McePG4ciRI6hevTpu376Njh07IjAwEA0bNnzsWLdu3cJ3332H06dPw83NDffv3y/ynOPGjUO7du1gMpnQoUMHHDx4EF26dMGaNWtw5coVTJ48GREREejWrRtmzpyJ8PDwUv0ZMLaHECIHMBTA62LHUtoeJK0aAP/ZBOUhQkgLSunZ0ovKNlFKNYSQAwD6APhR7HgYhmFsmf2uhLERXbp0wf79+wEAW7ZsQf/+/Qu3zZ8/H//73/9QvXp1AED16tXxzTffYN68eU8dZ/Xq1Rg5ciTc3NwAAJUrV35qH6VSiXbt2gEA5HI5GjdujKSkpMLtZrMZOp0OMpkMGzduRNeuXQuPx5Qr3QFcp5TeEDsQG3W6vLW5fsTDaRYMAwCQSCQIDQ1FSEgIGjdujL/++uux7YsWLYKzszNyc3P/8xihoaEICwsrch+j0Yi+ffuiZs2aaN68ORISEgAAN27cQJMmTRASEoIzZ84AKLgb+8Ybb0Cn01nnIhmmBNgI8kvq168fZsyYgW7duuHKlSv44IMPcPLkSQDA1atXMW7cuMf2b9q0KX788enBm5s3bwIAWrduDZ7nMW3aNHTu3PmZ583JycHvv/+OMWPGACgYWW7RogXq16+P1q1bo2fPnjh06NBzYyeESACMBqAqxiUztu8dAE9/C2Me4gCU1/I9h1HQ7CWQUhovdjCM+BQKBS5dugQAiIiIwDfffIPjx48Xbt+yZQteeeUV7Nq1C0OGDPnPYzzL2rVr4ebmhtjYWISHh+Prr7/G1q1bsWrVKsyZMwcBAQGYMGECduzYgRUrVmDQoEFQKpVWukqGKT6WIL+khg0bIiEhAVu2bEHXrl0f20YpfWqFfVHPAQXfmG/duoVjx44hKSkJbdq0QXR0NCpWrFjkvv3798fo0aMRGBgIABg0aBAGDRoEAJg+fTpGjx6NgwcPYsOGDfDz88OCBQueVTrL+cGDcRzhAHaIHQRjeyilZkLINgDvAZgldjyMbcnLy3vsrmNcXBw0Gg3mzZuH2bNnPzNBfhF79uzBtGnTAADvvvsuRo0aBUopZDIZ9Hp94d3Ph4M/ERERL3k1DPNyWIJsBWFhYRg3bhyOHTuGzMzMwufr16+PqKiox+Yb//PPP0UuMqhatSpatGgBmUyG6tWro06dOrh16xZeeeWVp/YdPnw4atWqhc8///ypbSkpKfj7778xdepUNGvWDGfOnMHEiRMRGRmJjh07PrYvpZQH8F3Jr5xhGDu0CcBaQsi3pV1/mbF9er0eoaGhMBgMuHfvHo4ePVq47eG0wTZt2uDGjRu4f/9+kdP/DAYDmjZtCqlUigkTJhS5ED05ORl+fn4AAKlUigoVKiAzMxMjR47E4MGDYTQasWrVKsyYMQMTJ04sk/KNDPM8LEG2gg8++AAVKlRAgwYNcOzYscLnx40bh969e6N9+/YICAhAQkICZs+eje3btz91jJ49e2LLli0YMmQIMjIycPPmzcLR4UdNmjQJubm5WLNmTZGxTJ48GTNnzgRQ8MZHCAHHcWwuF8MwD50BwAPIJYSwBLl8yAdQt6hKLI9Ojzhz5gwGDx6M6OhoEEIQHh6OXbt2geM49OrVC9u2bcPIkSOfOnhiYiJ8fX0RHx+P9u3bo0GDBqhR4/FCGUV9FyOEwN/fv/BzMzY2FikpKahbty4GDRoEk8mEmTNnonbt2i//E2CYYmIJshVUrVq1cC7wo0JDQ/H999+je/fuMJvNkMlkmDt3LkJDQ5/a980338Thw4cRFBQEiUSCefPmwcPDo/A4ly5dQlJSEr799lvUrVsXjRs3BgCMGjUKw4YNAwBcvHgRANCoUSMAwIcffogGDRrAz88PU6dOLY1LZxjGzlBKKSGkMQCF2LEwZcbyIh0UW7ZsiYyMDKSnpyM1NRW3bt0qvPNoMpkQGBhYZILs6+sLAAgMDETbtm1x8eLFpxLkqlWr4u7du6hatSosFgtyc3Ph7u7+2D4TJ07ErFmzsGTJEgwYMAABAQGYPn06Nm3aVOILZ5iSYgnyS9Boni6L2rZtW7Rt27bw/3v16oVevXr957EIIVi4cGGRNYsffruvWrVqkd/CH2rUqBHWrl1b+P+ff/55kdMwGIYp3yilJgAmseNgbMv169fB8zw8PDywaNEiTJs2Dd98803h9urVq+POnTuoVq1a4XPZ2dlQKpVwcnJCRkYGTp8+ja+++uqpY4eFhWH9+vVo2bIltm/fjvbt2z82jeL48eOoUqUKatWqBZ1OB47jIJFI2N1PRjQsQWYYhmGYcurhHGSgYBrE+vXrIZFIEB4ejoMHDz6279tvv11YgeKha9eu4eOPPwbHcRAEARMmTChcZzNlyhQ0bdoUYWFh+PDDDzFo0CDUrFkT7u7uj9Xop5Ri1qxZ+O233wAUrLMZMGAALBYLVqxYUco/AYYpGmFrNJ7vibac/v7+/jJRA7KS69ev6xMSElgNHabceTDvlmML1JjyKCgoKNbOO+nFxcTE1BQ7DsbxsRHk/2DvbTmfJSgoiP3dMwzDMAzDFKG8dpMq9yilFrFjYBiGYRiGsUUsQS6nCCEpYsfAMAzDMAxji1iCXEypqano168fatSogaCgIHTt2hU3b95EQkICFAoFQkNDERQUhBEjRkAQhMeeDwkJQatWrXDjxo0ij/0i/ewTExPRrl07NGrUCA0bNsSBAwcAsH72DMMwDMMw1sLmoRYDpRRvv/023n///cIVuJcuXUJaWhr8/PxQo0YNXLp0CRaLBe3bt8fu3bvRuHHjwucBYNWqVZg9ezbWr1//1PFfpJ/9rFmz0KdPH3zyySeIiYlB165dkZCQwPrZMwzDMP9Jo9EkFtXN1V5oNJpEsWNgygeWIBfDn3/+CZlMhhEjRhQ+97A8TkJCQuFzUqkUrVq1QmxsbGFDj4ee7HVfXIQQ5OXlAQByc3MLC7SzfvYMwzDMf0lMTGwvdgwMYw9YglwM0dHRaNKkyX/up9PpEBkZiRkzZgAA4uLiEBoaivz8fOh0Opw7d67I171IP/tp06ahU6dOWLp0KbRaLf744w8AKFE/e0KIDMBGAKr/vCiGcRysvBvDMAzzXCxBtqKHiTAhBD169ECXLl2QkJDw2BSLrVu3Yvjw4Th06NBTr3+RfvZbtmzBkCFDMHbsWJw5cwaDBg1CdHR0SfvZWwD8BJYgM+XLXFYDmWEYhnkeliAXQ/369bF9+/Znbn80EX6WsLAwDB06tMhtL9LPfu3atYXJdcuWLWEwGJCRkYHKlSsX7vOi/ewfJAlHnxswwzAMwzBMOcOqWBRD+/btYTQasXr16sLn/v77bxw/fvyFj3Hq1Kmnkl6goJ+90WgEgMJ+9kUtpPD390dkZCSAghafBoMBnp6ehdtZP3uGYRiGYZiXw0aQi4EQgl27duHzzz/HnDlz4OzsjICAACxevPi5r3s49YJSCrlcjjVr1jy1z4v2s1+wYAE++ugjLFq0CIQQrFu3rnCeMetnzzAMwzAM8/IIm4r3fPbet/5ZWD97hmEYhmGYorEpFgzDMAzDMAzzCJYgMwzDMAzDMMwjWILMMAzDMAzDMI9gi/T+g7235XwW1q6TYRiGYRimaGyRHsMwDMMwDMM8gk2xYBiGYRiGYZhHsASZYRiGYRiGYR7xf1DZMw5mFFF/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w_bl, title='Sharpe Black Litterman', others=0.05, nrow=25,\n",
    "                  cmap = \"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.2634%</td>\n",
       "      <td>4.3887%</td>\n",
       "      <td>2.1706%</td>\n",
       "      <td>6.9872%</td>\n",
       "      <td>3.2390%</td>\n",
       "      <td>0.0791%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.8377%</td>\n",
       "      <td>...</td>\n",
       "      <td>11.4508%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>14.9183%</td>\n",
       "      <td>0.1626%</td>\n",
       "      <td>6.4196%</td>\n",
       "      <td>4.0904%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.3446%</td>\n",
       "      <td>0.0001%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.8898%</td>\n",
       "      <td>7.9755%</td>\n",
       "      <td>2.9166%</td>\n",
       "      <td>1.6685%</td>\n",
       "      <td>5.5406%</td>\n",
       "      <td>4.1246%</td>\n",
       "      <td>0.5331%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.7285%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.3216%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.7680%</td>\n",
       "      <td>3.0750%</td>\n",
       "      <td>4.0054%</td>\n",
       "      <td>5.1909%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.0263%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.6334%</td>\n",
       "      <td>8.7146%</td>\n",
       "      <td>2.2991%</td>\n",
       "      <td>1.2960%</td>\n",
       "      <td>4.9345%</td>\n",
       "      <td>4.5211%</td>\n",
       "      <td>0.6108%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.2609%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4025%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.6735%</td>\n",
       "      <td>4.2328%</td>\n",
       "      <td>3.1396%</td>\n",
       "      <td>5.5898%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.4976%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0887%</td>\n",
       "      <td>3.1881%</td>\n",
       "      <td>9.2750%</td>\n",
       "      <td>1.8154%</td>\n",
       "      <td>0.9588%</td>\n",
       "      <td>4.4142%</td>\n",
       "      <td>4.8317%</td>\n",
       "      <td>0.6477%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.8553%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6758%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.9908%</td>\n",
       "      <td>5.1234%</td>\n",
       "      <td>2.4056%</td>\n",
       "      <td>5.8917%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1125%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2027%</td>\n",
       "      <td>3.6394%</td>\n",
       "      <td>9.7407%</td>\n",
       "      <td>1.4013%</td>\n",
       "      <td>0.6314%</td>\n",
       "      <td>3.9448%</td>\n",
       "      <td>5.0942%</td>\n",
       "      <td>0.6645%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4769%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0409%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.5085%</td>\n",
       "      <td>5.8672%</td>\n",
       "      <td>1.7321%</td>\n",
       "      <td>6.1419%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.8073%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      APA      BA     BAX     BMY   CMCSA     CNP     CPB      DE     HPQ  \\\n",
       "0 0.0000% 0.0000% 5.2634% 4.3887% 2.1706% 6.9872% 3.2390% 0.0791% 0.0000%   \n",
       "1 0.0000% 1.8898% 7.9755% 2.9166% 1.6685% 5.5406% 4.1246% 0.5331% 0.0000%   \n",
       "2 0.0000% 2.6334% 8.7146% 2.2991% 1.2960% 4.9345% 4.5211% 0.6108% 0.0000%   \n",
       "3 0.0887% 3.1881% 9.2750% 1.8154% 0.9588% 4.4142% 4.8317% 0.6477% 0.0000%   \n",
       "4 0.2027% 3.6394% 9.7407% 1.4013% 0.6314% 3.9448% 5.0942% 0.6645% 0.0000%   \n",
       "\n",
       "      JCI  ...       NI    PCAR      PSA     SEE       T     TGT     TMO  \\\n",
       "0 2.8377%  ... 11.4508% 0.0000% 14.9183% 0.1626% 6.4196% 4.0904% 0.0000%   \n",
       "1 1.7285%  ...  9.3216% 0.0000%  9.7680% 3.0750% 4.0054% 5.1909% 0.0000%   \n",
       "2 1.2609%  ...  8.4025% 0.0000%  7.6735% 4.2328% 3.1396% 5.5898% 0.0000%   \n",
       "3 0.8553%  ...  7.6758% 0.0000%  5.9908% 5.1234% 2.4056% 5.8917% 0.0000%   \n",
       "4 0.4769%  ...  7.0409% 0.0000%  4.5085% 5.8672% 1.7321% 6.1419% 0.0000%   \n",
       "\n",
       "      TXT      VZ    ZION  \n",
       "0 0.0000% 8.3446% 0.0001%  \n",
       "1 0.0000% 7.0263% 0.0000%  \n",
       "2 0.0000% 6.4976% 0.0000%  \n",
       "3 0.0000% 6.1125% 0.0000%  \n",
       "4 0.0000% 5.8073% 0.0000%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "mu = port.mu_bl # Expected returns of Black Litterman model\n",
    "cov = port.cov_bl # Covariance matrix of Black Litterman model\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "ax = plf.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                       rf=rf, alpha=0.05, cmap='viridis', w=w_bl, label=label,\n",
    "                       marker='*', s=16, c='r', height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Black Litterman Mean Risk Portfolios\n",
    "\n",
    "When we use risk measures differents than Standard Deviation, Riskfolio-Lib only considers the vector of expected returns, and use historical returns to calculate risk measures.\n",
    "\n",
    "### 3.4 Calculate Black Litterman Portfolios for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'WR', 'MDD', 'ADD', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "port.alpha = 0.05\n",
    "\n",
    "for i in rms:\n",
    "    if i == 'MV':\n",
    "        hist = False\n",
    "    else:\n",
    "        hist = True\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_44f72_row0_col0{\n",
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       "            color:  #000000;\n",
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       "            background-color:  #feffe1;\n",
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       "            background-color:  #e0f3a8;\n",
       "            color:  #000000;\n",
       "        }#T_44f72_row23_col1{\n",
       "            background-color:  #def2a7;\n",
       "            color:  #000000;\n",
       "        }#T_44f72_row23_col2{\n",
       "            background-color:  #f1fab5;\n",
       "            color:  #000000;\n",
       "        }#T_44f72_row23_col9{\n",
       "            background-color:  #dff3a8;\n",
       "            color:  #000000;\n",
       "        }#T_44f72_row24_col1{\n",
       "            background-color:  #fbfdce;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fdfed9;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_44f72_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >UCI</th>        <th class=\"col_heading level0 col12\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_44f72_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_44f72_row0_col0\" class=\"data row0 col0\" >0.53%</td>\n",
       "                        <td id=\"T_44f72_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_44f72_row1_col0\" class=\"data row1 col0\" >5.03%</td>\n",
       "                        <td id=\"T_44f72_row1_col1\" class=\"data row1 col1\" >6.11%</td>\n",
       "                        <td id=\"T_44f72_row1_col2\" class=\"data row1 col2\" >3.10%</td>\n",
       "                        <td id=\"T_44f72_row1_col3\" class=\"data row1 col3\" >4.17%</td>\n",
       "                        <td id=\"T_44f72_row1_col4\" class=\"data row1 col4\" >2.81%</td>\n",
       "                        <td id=\"T_44f72_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row1_col9\" class=\"data row1 col9\" >7.94%</td>\n",
       "                        <td id=\"T_44f72_row1_col10\" class=\"data row1 col10\" >2.64%</td>\n",
       "                        <td id=\"T_44f72_row1_col11\" class=\"data row1 col11\" >6.80%</td>\n",
       "                        <td id=\"T_44f72_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_44f72_row2_col0\" class=\"data row2 col0\" >11.17%</td>\n",
       "                        <td id=\"T_44f72_row2_col1\" class=\"data row2 col1\" >8.35%</td>\n",
       "                        <td id=\"T_44f72_row2_col2\" class=\"data row2 col2\" >10.66%</td>\n",
       "                        <td id=\"T_44f72_row2_col3\" class=\"data row2 col3\" >8.86%</td>\n",
       "                        <td id=\"T_44f72_row2_col4\" class=\"data row2 col4\" >10.66%</td>\n",
       "                        <td id=\"T_44f72_row2_col5\" class=\"data row2 col5\" >11.94%</td>\n",
       "                        <td id=\"T_44f72_row2_col6\" class=\"data row2 col6\" >7.32%</td>\n",
       "                        <td id=\"T_44f72_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row2_col9\" class=\"data row2 col9\" >3.89%</td>\n",
       "                        <td id=\"T_44f72_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row2_col11\" class=\"data row2 col11\" >4.66%</td>\n",
       "                        <td id=\"T_44f72_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_44f72_row3_col0\" class=\"data row3 col0\" >0.01%</td>\n",
       "                        <td id=\"T_44f72_row3_col1\" class=\"data row3 col1\" >2.18%</td>\n",
       "                        <td id=\"T_44f72_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col3\" class=\"data row3 col3\" >2.87%</td>\n",
       "                        <td id=\"T_44f72_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col6\" class=\"data row3 col6\" >4.13%</td>\n",
       "                        <td id=\"T_44f72_row3_col7\" class=\"data row3 col7\" >8.94%</td>\n",
       "                        <td id=\"T_44f72_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row3_col12\" class=\"data row3 col12\" >1.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_44f72_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col3\" class=\"data row4 col3\" >0.06%</td>\n",
       "                        <td id=\"T_44f72_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_44f72_row5_col0\" class=\"data row5 col0\" >2.34%</td>\n",
       "                        <td id=\"T_44f72_row5_col1\" class=\"data row5 col1\" >3.36%</td>\n",
       "                        <td id=\"T_44f72_row5_col2\" class=\"data row5 col2\" >1.91%</td>\n",
       "                        <td id=\"T_44f72_row5_col3\" class=\"data row5 col3\" >3.72%</td>\n",
       "                        <td id=\"T_44f72_row5_col4\" class=\"data row5 col4\" >1.58%</td>\n",
       "                        <td id=\"T_44f72_row5_col5\" class=\"data row5 col5\" >2.27%</td>\n",
       "                        <td id=\"T_44f72_row5_col6\" class=\"data row5 col6\" >2.19%</td>\n",
       "                        <td id=\"T_44f72_row5_col7\" class=\"data row5 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row5_col8\" class=\"data row5 col8\" >52.20%</td>\n",
       "                        <td id=\"T_44f72_row5_col9\" class=\"data row5 col9\" >11.84%</td>\n",
       "                        <td id=\"T_44f72_row5_col10\" class=\"data row5 col10\" >31.88%</td>\n",
       "                        <td id=\"T_44f72_row5_col11\" class=\"data row5 col11\" >16.25%</td>\n",
       "                        <td id=\"T_44f72_row5_col12\" class=\"data row5 col12\" >30.14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_44f72_row6_col0\" class=\"data row6 col0\" >5.85%</td>\n",
       "                        <td id=\"T_44f72_row6_col1\" class=\"data row6 col1\" >2.42%</td>\n",
       "                        <td id=\"T_44f72_row6_col2\" class=\"data row6 col2\" >6.71%</td>\n",
       "                        <td id=\"T_44f72_row6_col3\" class=\"data row6 col3\" >3.94%</td>\n",
       "                        <td id=\"T_44f72_row6_col4\" class=\"data row6 col4\" >7.18%</td>\n",
       "                        <td id=\"T_44f72_row6_col5\" class=\"data row6 col5\" >10.58%</td>\n",
       "                        <td id=\"T_44f72_row6_col6\" class=\"data row6 col6\" >17.56%</td>\n",
       "                        <td id=\"T_44f72_row6_col7\" class=\"data row6 col7\" >15.82%</td>\n",
       "                        <td id=\"T_44f72_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row6_col9\" class=\"data row6 col9\" >2.40%</td>\n",
       "                        <td id=\"T_44f72_row6_col10\" class=\"data row6 col10\" >0.17%</td>\n",
       "                        <td id=\"T_44f72_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_44f72_row7_col0\" class=\"data row7 col0\" >0.63%</td>\n",
       "                        <td id=\"T_44f72_row7_col1\" class=\"data row7 col1\" >1.61%</td>\n",
       "                        <td id=\"T_44f72_row7_col2\" class=\"data row7 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row7_col3\" class=\"data row7 col3\" >0.19%</td>\n",
       "                        <td id=\"T_44f72_row7_col4\" class=\"data row7 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row7_col5\" class=\"data row7 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row7_col8\" class=\"data row7 col8\" >5.92%</td>\n",
       "                        <td id=\"T_44f72_row7_col9\" class=\"data row7 col9\" >0.06%</td>\n",
       "                        <td id=\"T_44f72_row7_col10\" class=\"data row7 col10\" >0.05%</td>\n",
       "                        <td id=\"T_44f72_row7_col11\" class=\"data row7 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_44f72_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col1\" class=\"data row8 col1\" >0.07%</td>\n",
       "                        <td id=\"T_44f72_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_44f72_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col9\" class=\"data row9 col9\" >1.18%</td>\n",
       "                        <td id=\"T_44f72_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_44f72_row10_col0\" class=\"data row10 col0\" >6.41%</td>\n",
       "                        <td id=\"T_44f72_row10_col1\" class=\"data row10 col1\" >11.68%</td>\n",
       "                        <td id=\"T_44f72_row10_col2\" class=\"data row10 col2\" >7.79%</td>\n",
       "                        <td id=\"T_44f72_row10_col3\" class=\"data row10 col3\" >12.03%</td>\n",
       "                        <td id=\"T_44f72_row10_col4\" class=\"data row10 col4\" >7.43%</td>\n",
       "                        <td id=\"T_44f72_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_44f72_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_44f72_row12_col0\" class=\"data row12 col0\" >20.51%</td>\n",
       "                        <td id=\"T_44f72_row12_col1\" class=\"data row12 col1\" >14.44%</td>\n",
       "                        <td id=\"T_44f72_row12_col2\" class=\"data row12 col2\" >20.37%</td>\n",
       "                        <td id=\"T_44f72_row12_col3\" class=\"data row12 col3\" >15.55%</td>\n",
       "                        <td id=\"T_44f72_row12_col4\" class=\"data row12 col4\" >20.52%</td>\n",
       "                        <td id=\"T_44f72_row12_col5\" class=\"data row12 col5\" >24.15%</td>\n",
       "                        <td id=\"T_44f72_row12_col6\" class=\"data row12 col6\" >15.98%</td>\n",
       "                        <td id=\"T_44f72_row12_col7\" class=\"data row12 col7\" >35.57%</td>\n",
       "                        <td id=\"T_44f72_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row12_col9\" class=\"data row12 col9\" >16.64%</td>\n",
       "                        <td id=\"T_44f72_row12_col10\" class=\"data row12 col10\" >8.81%</td>\n",
       "                        <td id=\"T_44f72_row12_col11\" class=\"data row12 col11\" >16.23%</td>\n",
       "                        <td id=\"T_44f72_row12_col12\" class=\"data row12 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_44f72_row13_col0\" class=\"data row13 col0\" >6.20%</td>\n",
       "                        <td id=\"T_44f72_row13_col1\" class=\"data row13 col1\" >4.50%</td>\n",
       "                        <td id=\"T_44f72_row13_col2\" class=\"data row13 col2\" >3.96%</td>\n",
       "                        <td id=\"T_44f72_row13_col3\" class=\"data row13 col3\" >3.46%</td>\n",
       "                        <td id=\"T_44f72_row13_col4\" class=\"data row13 col4\" >3.96%</td>\n",
       "                        <td id=\"T_44f72_row13_col5\" class=\"data row13 col5\" >3.02%</td>\n",
       "                        <td id=\"T_44f72_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_44f72_row14_col0\" class=\"data row14 col0\" >16.89%</td>\n",
       "                        <td id=\"T_44f72_row14_col1\" class=\"data row14 col1\" >20.92%</td>\n",
       "                        <td id=\"T_44f72_row14_col2\" class=\"data row14 col2\" >22.07%</td>\n",
       "                        <td id=\"T_44f72_row14_col3\" class=\"data row14 col3\" >21.89%</td>\n",
       "                        <td id=\"T_44f72_row14_col4\" class=\"data row14 col4\" >22.10%</td>\n",
       "                        <td id=\"T_44f72_row14_col5\" class=\"data row14 col5\" >20.19%</td>\n",
       "                        <td id=\"T_44f72_row14_col6\" class=\"data row14 col6\" >26.01%</td>\n",
       "                        <td id=\"T_44f72_row14_col7\" class=\"data row14 col7\" >19.67%</td>\n",
       "                        <td id=\"T_44f72_row14_col8\" class=\"data row14 col8\" >37.83%</td>\n",
       "                        <td id=\"T_44f72_row14_col9\" class=\"data row14 col9\" >40.30%</td>\n",
       "                        <td id=\"T_44f72_row14_col10\" class=\"data row14 col10\" >45.06%</td>\n",
       "                        <td id=\"T_44f72_row14_col11\" class=\"data row14 col11\" >42.22%</td>\n",
       "                        <td id=\"T_44f72_row14_col12\" class=\"data row14 col12\" >52.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_44f72_row15_col0\" class=\"data row15 col0\" >4.86%</td>\n",
       "                        <td id=\"T_44f72_row15_col1\" class=\"data row15 col1\" >7.82%</td>\n",
       "                        <td id=\"T_44f72_row15_col2\" class=\"data row15 col2\" >6.03%</td>\n",
       "                        <td id=\"T_44f72_row15_col3\" class=\"data row15 col3\" >6.10%</td>\n",
       "                        <td id=\"T_44f72_row15_col4\" class=\"data row15 col4\" >6.13%</td>\n",
       "                        <td id=\"T_44f72_row15_col5\" class=\"data row15 col5\" >1.24%</td>\n",
       "                        <td id=\"T_44f72_row15_col6\" class=\"data row15 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row15_col9\" class=\"data row15 col9\" >4.06%</td>\n",
       "                        <td id=\"T_44f72_row15_col10\" class=\"data row15 col10\" >3.45%</td>\n",
       "                        <td id=\"T_44f72_row15_col11\" class=\"data row15 col11\" >3.38%</td>\n",
       "                        <td id=\"T_44f72_row15_col12\" class=\"data row15 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_44f72_row16_col0\" class=\"data row16 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row16_col12\" class=\"data row16 col12\" >0.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_44f72_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col2\" class=\"data row17 col2\" >1.10%</td>\n",
       "                        <td id=\"T_44f72_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col4\" class=\"data row17 col4\" >1.26%</td>\n",
       "                        <td id=\"T_44f72_row17_col5\" class=\"data row17 col5\" >8.31%</td>\n",
       "                        <td id=\"T_44f72_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row17_col11\" class=\"data row17 col11\" >0.33%</td>\n",
       "                        <td id=\"T_44f72_row17_col12\" class=\"data row17 col12\" >0.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_44f72_row18_col0\" class=\"data row18 col0\" >8.17%</td>\n",
       "                        <td id=\"T_44f72_row18_col1\" class=\"data row18 col1\" >3.02%</td>\n",
       "                        <td id=\"T_44f72_row18_col2\" class=\"data row18 col2\" >4.23%</td>\n",
       "                        <td id=\"T_44f72_row18_col3\" class=\"data row18 col3\" >3.11%</td>\n",
       "                        <td id=\"T_44f72_row18_col4\" class=\"data row18 col4\" >4.36%</td>\n",
       "                        <td id=\"T_44f72_row18_col5\" class=\"data row18 col5\" >4.94%</td>\n",
       "                        <td id=\"T_44f72_row18_col6\" class=\"data row18 col6\" >8.73%</td>\n",
       "                        <td id=\"T_44f72_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row18_col8\" class=\"data row18 col8\" >4.04%</td>\n",
       "                        <td id=\"T_44f72_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row18_col12\" class=\"data row18 col12\" >4.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_44f72_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col1\" class=\"data row19 col1\" >2.51%</td>\n",
       "                        <td id=\"T_44f72_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col3\" class=\"data row19 col3\" >1.23%</td>\n",
       "                        <td id=\"T_44f72_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row19_col11\" class=\"data row19 col11\" >0.30%</td>\n",
       "                        <td id=\"T_44f72_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_44f72_row20_col0\" class=\"data row20 col0\" >6.88%</td>\n",
       "                        <td id=\"T_44f72_row20_col1\" class=\"data row20 col1\" >4.85%</td>\n",
       "                        <td id=\"T_44f72_row20_col2\" class=\"data row20 col2\" >8.78%</td>\n",
       "                        <td id=\"T_44f72_row20_col3\" class=\"data row20 col3\" >5.67%</td>\n",
       "                        <td id=\"T_44f72_row20_col4\" class=\"data row20 col4\" >9.17%</td>\n",
       "                        <td id=\"T_44f72_row20_col5\" class=\"data row20 col5\" >10.06%</td>\n",
       "                        <td id=\"T_44f72_row20_col6\" class=\"data row20 col6\" >18.08%</td>\n",
       "                        <td id=\"T_44f72_row20_col7\" class=\"data row20 col7\" >20.00%</td>\n",
       "                        <td id=\"T_44f72_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row20_col9\" class=\"data row20 col9\" >0.89%</td>\n",
       "                        <td id=\"T_44f72_row20_col10\" class=\"data row20 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row20_col11\" class=\"data row20 col11\" >0.58%</td>\n",
       "                        <td id=\"T_44f72_row20_col12\" class=\"data row20 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_44f72_row21_col0\" class=\"data row21 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col9\" class=\"data row21 col9\" >0.19%</td>\n",
       "                        <td id=\"T_44f72_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_44f72_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_44f72_row23_col0\" class=\"data row23 col0\" >4.51%</td>\n",
       "                        <td id=\"T_44f72_row23_col1\" class=\"data row23 col1\" >4.77%</td>\n",
       "                        <td id=\"T_44f72_row23_col2\" class=\"data row23 col2\" >3.28%</td>\n",
       "                        <td id=\"T_44f72_row23_col3\" class=\"data row23 col3\" >6.02%</td>\n",
       "                        <td id=\"T_44f72_row23_col4\" class=\"data row23 col4\" >2.84%</td>\n",
       "                        <td id=\"T_44f72_row23_col5\" class=\"data row23 col5\" >3.31%</td>\n",
       "                        <td id=\"T_44f72_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row23_col9\" class=\"data row23 col9\" >9.12%</td>\n",
       "                        <td id=\"T_44f72_row23_col10\" class=\"data row23 col10\" >7.94%</td>\n",
       "                        <td id=\"T_44f72_row23_col11\" class=\"data row23 col11\" >9.24%</td>\n",
       "                        <td id=\"T_44f72_row23_col12\" class=\"data row23 col12\" >9.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_44f72_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_44f72_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col1\" class=\"data row24 col1\" >1.39%</td>\n",
       "                        <td id=\"T_44f72_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col3\" class=\"data row24 col3\" >1.13%</td>\n",
       "                        <td id=\"T_44f72_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col9\" class=\"data row24 col9\" >1.50%</td>\n",
       "                        <td id=\"T_44f72_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "                        <td id=\"T_44f72_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fa32e40b730>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
